{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1743224e-c41c-4e5a-b53e-389986c71fac",
   "metadata": {},
   "source": [
    "<center><h1>基于天气数据集的XGBoost分类实战</h1>Author:dsy Time:2021-12-09</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67b3444-287a-41a9-9681-e399fd0cedae",
   "metadata": {},
   "source": [
    "# 前言"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2010ae0a-008b-49a8-9757-adf9232464ec",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 0.1 XGBoost的重要参数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c8cd19-30e8-492d-9236-0c7640415647",
   "metadata": {},
   "source": [
    "1. eta[默认0.3]\n",
    "通过为每一颗树增加权重，提高模型的鲁棒性。\n",
    "典型值为0.01-0.2。\n",
    "\n",
    "2. min_child_weight[默认1]\n",
    "决定最小叶子节点样本权重和。\n",
    "这个参数可以避免过拟合。当它的值较大时，可以避免模型学习到局部的特殊样本。\n",
    "但是如果这个值过高，则会导致模型拟合不充分。\n",
    "\n",
    "3. max_depth[默认6]\n",
    "这个值也是用来避免过拟合的。max_depth越大，模型会学到更具体更局部的样本。\n",
    "典型值：3-10\n",
    "\n",
    "4. max_leaf_nodes\n",
    "树上最大的节点或叶子的数量。\n",
    "可以替代max_depth的作用。\n",
    "这个参数的定义会导致忽略max_depth参数。\n",
    "\n",
    "5. gamma[默认0]\n",
    "在节点分裂时，只有分裂后损失函数的值下降了，才会分裂这个节点。Gamma指定了节点分裂所需的最小损失函数下降值。 这个参数的值越大，算法越保守。这个参数的值和损失函数息息相关。\n",
    "\n",
    "6. max_delta_step[默认0]\n",
    "这参数限制每棵树权重改变的最大步长。如果这个参数的值为0，那就意味着没有约束。如果它被赋予了某个正值，那么它会让这个算法更加保守。\n",
    "但是当各类别的样本十分不平衡时，它对分类问题是很有帮助的。\n",
    "\n",
    "7. subsample[默认1]\n",
    "这个参数控制对于每棵树，随机采样的比例。\n",
    "减小这个参数的值，算法会更加保守，避免过拟合。但是，如果这个值设置得过小，它可能会导致欠拟合。\n",
    "典型值：0.5-1\n",
    "\n",
    "8. colsample_bytree[默认1]\n",
    "用来控制每棵随机采样的列数的占比(每一列是一个特征)。\n",
    "典型值：0.5-1\n",
    "\n",
    "9. colsample_bylevel[默认1]\n",
    "用来控制树的每一级的每一次分裂，对列数的采样的占比。\n",
    "subsample参数和colsample_bytree参数可以起到相同的作用，一般用不到。\n",
    "\n",
    "10. lambda[默认1]\n",
    "权重的L2正则化项。(和Ridge regression类似)。\n",
    "这个参数是用来控制XGBoost的正则化部分的。虽然大部分数据科学家很少用到这个参数，但是这个参数在减少过拟合上还是可以挖掘出更多用处的。\n",
    "\n",
    "11. alpha[默认1]\n",
    "权重的L1正则化项。(和Lasso regression类似)。\n",
    "可以应用在很高维度的情况下，使得算法的速度更快。\n",
    "\n",
    "12. scale_pos_weight[默认1]\n",
    "在各类别样本十分不平衡时，把这个参数设定为一个正值，可以使算法更快收敛。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d72998-dee8-4b03-838e-2ac582dda8d9",
   "metadata": {},
   "source": [
    "## 0.2 XGBoost原理粗略讲解"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873dee0d-6732-49ae-a5df-a5fc7d2a079b",
   "metadata": {},
   "source": [
    "XGBoost底层实现了GBDT算法，并对GBDT算法做了一系列优化：\n",
    "1. 对目标函数进行了泰勒展示的二阶展开，可以更加高效拟合误差。\n",
    "2. 提出了一种估计分裂点的算法加速CART树的构建过程，同时可以处理稀疏数据。\n",
    "3. 提出了一种树的并行策略加速迭代。\n",
    "4. 为模型的分布式算法进行了底层优化。\n",
    "\n",
    "XGBoost是基于CART树的集成模型，它的思想是串联多个决策树模型共同进行决策。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa86be7d-4948-447e-9091-8d223108e9b8",
   "metadata": {},
   "source": [
    "那么如何串联呢？XGBoost采用迭代预测误差的方法串联。举个通俗的例子，我们现在需要预测一辆车价值3000元。我们构建决策树1训练后预测为2600元，我们发现有400元的误差，那么决策树2的训练目标为400元，但决策树2的预测结果为350元，还存在50元的误差就交给第三棵树……以此类推，每一颗树用来估计之前所有树的误差，最后所有树预测结果的求和就是最终预测结果！"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee9e58f-e217-4232-bca8-2d8b19ed666c",
   "metadata": {},
   "source": [
    "XGBoost的基模型是CART回归树，它有两个特点：（1）CART树，是一颗二叉树。（2）回归树，最后拟合结果是连续值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18eb2b01-214b-42f8-9e13-1e6b96d98e71",
   "metadata": {},
   "source": [
    "XGBoost模型可以表示为以下形式，我们约定$f_t(x)$表示前t颗树的和， $h_t(x)$表示第 t颗决策树，模型定义如下：\n",
    "$$\n",
    "f_t(x) = \\sum_{t=1}^{T}h_t(x)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81283adf-a985-48d8-8397-37c56206cac0",
   "metadata": {},
   "source": [
    "由于模型递归生成，第 t步的模型由第 t−1步的模型形成，可以写成：\n",
    "$$\n",
    "f_t(x) = f_{t-1}(x)+h_t(x)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760d3704-8a4f-4fb8-b54d-04e496362d52",
   "metadata": {},
   "source": [
    "每次需要加上的树 $h_t(x)$是之前树求和的误差："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f611a391-c5a5-4d1f-bcde-cced6f36b57c",
   "metadata": {},
   "source": [
    "$$\n",
    "r_{t,i} = y_i - f_{m-1}(x_i)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d42de172-b380-49cc-9ea4-ed3f9f720653",
   "metadata": {},
   "source": [
    "我们每一步只要拟合一颗输出为 $r_{t,i}$的CART树加到 $f_{t−1}(x)$就可以了。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea37b530-8b6f-4336-8c96-e7597f9c32d2",
   "metadata": {},
   "source": [
    "# Step1：函数库导入"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d40933-c328-46ca-890a-dc77f826bb31",
   "metadata": {},
   "source": [
    "在实践的最开始，我们首先需要导入一些基础的函数库包括：numpy （Python进行科学计算的基础软件包），pandas（pandas是一种快速，强大，灵活且易于使用的开源数据分析和处理工具），matplotlib和seaborn绘图。"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c7885081-c46b-464b-ab1d-de3585f5378b",
   "metadata": {},
   "source": [
    "#导入需要用到的数据集\n",
    "!curl -O https://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/7XGBoost/train.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b127297-126c-43d1-aeda-4d03cd0e4f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "##  基础函数库\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "## 绘图函数库\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ee9c6d2-1889-42c3-ac31-f30c0909e5d1",
   "metadata": {},
   "source": [
    "# Step2：数据读取/载入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d8f64174-5808-48c6-88e7-8322f8ac6f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 我们利用Pandas自带的read_csv函数读取并转化为DataFrame格式\n",
    "data = pd.read_csv('data/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4926de63-fc65-4ab4-959a-0915f31e322d",
   "metadata": {},
   "source": [
    "本次我们选择天气数据集进行方法的尝试训练，现在有一些由气象站提供的每日降雨数据，我们需要根据历史降雨数据来预测明天会下雨的概率。样例涉及到的测试集数据test.csv与train.csv的格式完全相同，但其RainTomorrow未给出，为预测变量。\n",
    "\n",
    "数据的各个特征描述如下："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65c72f8-bdae-4313-9d7e-c699d33a8ec4",
   "metadata": {},
   "source": [
    "特征名称|意义|取值范围\n",
    "--|--|--\n",
    "Date|日期|字符串\n",
    "Location|气象站的地址|字符串\n",
    "MinTemp|最低温度|实数\n",
    "MaxTemp|最高温度|实数\n",
    "Rainfall|降雨量|实数\n",
    "Evaporation|蒸发量|实数\n",
    "Sunshine|光照时间|实数\n",
    "WindGustDir|最强的风的方向|字符串\n",
    "WindGustSpeed|最强的风的速度|实数\n",
    "WindDir9am|早上9点的风向|字符串\n",
    "WindDir3pm|下午3点的风向|字符串\n",
    "WindSpeed9am|早上9点的风速|实数\n",
    "WindSpeed3pm|下午3点的风速|实数\n",
    "Humidity9am|早上9点的湿度|实数\n",
    "Humidity3pm|下午3点的湿度|实数\n",
    "Pressure9am|早上9点的大气压|实数\n",
    "Pressure3pm|早上3点的大气压|实数\n",
    "Cloud9am|早上9点的云指数|实数\n",
    "Cloud3pm|早上3点的云指数|实数\n",
    "Temp9am|早上9点的温度|实数\n",
    "Temp3pm|早上3点的温度|实数\n",
    "RainToday|今天是否下雨|No，Yes\n",
    "RainTomorrow|明天是否下雨|No，Yes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b399667-01c0-4159-bbb9-3fcedc1c6a49",
   "metadata": {},
   "source": [
    "# Step3：数据信息简单查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "637edc19-9ad1-4001-8d26-316c45d1ad73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 106644 entries, 0 to 106643\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   Date           106644 non-null  object \n",
      " 1   Location       106644 non-null  object \n",
      " 2   MinTemp        106183 non-null  float64\n",
      " 3   MaxTemp        106413 non-null  float64\n",
      " 4   Rainfall       105610 non-null  float64\n",
      " 5   Evaporation    60974 non-null   float64\n",
      " 6   Sunshine       55718 non-null   float64\n",
      " 7   WindGustDir    99660 non-null   object \n",
      " 8   WindGustSpeed  99702 non-null   float64\n",
      " 9   WindDir9am     99166 non-null   object \n",
      " 10  WindDir3pm     103788 non-null  object \n",
      " 11  WindSpeed9am   105643 non-null  float64\n",
      " 12  WindSpeed3pm   104653 non-null  float64\n",
      " 13  Humidity9am    105327 non-null  float64\n",
      " 14  Humidity3pm    103932 non-null  float64\n",
      " 15  Pressure9am    96107 non-null   float64\n",
      " 16  Pressure3pm    96123 non-null   float64\n",
      " 17  Cloud9am       66303 non-null   float64\n",
      " 18  Cloud3pm       63691 non-null   float64\n",
      " 19  Temp9am        105983 non-null  float64\n",
      " 20  Temp3pm        104599 non-null  float64\n",
      " 21  RainToday      105610 non-null  object \n",
      " 22  RainTomorrow   106644 non-null  object \n",
      "dtypes: float64(16), object(7)\n",
      "memory usage: 18.7+ MB\n"
     ]
    }
   ],
   "source": [
    "## 利用.info()查看数据的整体信息\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9dc5dd5-d67f-4f60-9940-6987acd45c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "        Date     Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n0  2012/1/19  MountGinini     12.1     23.1       0.0          NaN       NaN   \n1  2015/4/13         Nhil     10.2     24.7       0.0          NaN       NaN   \n2   2010/8/5    Nuriootpa     -0.4     11.0       3.6          0.4       1.6   \n3  2013/3/18     Adelaide     13.2     22.6       0.0         15.4      11.0   \n4  2011/2/16         Sale     14.1     28.6       0.0          6.6       6.7   \n\n  WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \\\n0           W           30.0          N  ...        60.0         54.0   \n1           E           39.0          E  ...        63.0         33.0   \n2           W           28.0          N  ...        97.0         78.0   \n3          SE           44.0          E  ...        47.0         34.0   \n4           E           28.0         NE  ...        92.0         42.0   \n\n   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n0          NaN          NaN       NaN       NaN     17.0     22.0         No   \n1       1021.9       1017.9       NaN       NaN     12.5     23.7         No   \n2       1025.9       1025.3       7.0       8.0      3.9      9.0        Yes   \n3       1025.0       1022.2       NaN       NaN     15.2     21.7         No   \n4       1018.0       1014.1       4.0       7.0     19.1     28.2         No   \n\n   RainTomorrow  \n0            No  \n1           Yes  \n2            No  \n3            No  \n4            No  \n\n[5 rows x 23 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Location</th>\n      <th>MinTemp</th>\n      <th>MaxTemp</th>\n      <th>Rainfall</th>\n      <th>Evaporation</th>\n      <th>Sunshine</th>\n      <th>WindGustDir</th>\n      <th>WindGustSpeed</th>\n      <th>WindDir9am</th>\n      <th>...</th>\n      <th>Humidity9am</th>\n      <th>Humidity3pm</th>\n      <th>Pressure9am</th>\n      <th>Pressure3pm</th>\n      <th>Cloud9am</th>\n      <th>Cloud3pm</th>\n      <th>Temp9am</th>\n      <th>Temp3pm</th>\n      <th>RainToday</th>\n      <th>RainTomorrow</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2012/1/19</td>\n      <td>MountGinini</td>\n      <td>12.1</td>\n      <td>23.1</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>W</td>\n      <td>30.0</td>\n      <td>N</td>\n      <td>...</td>\n      <td>60.0</td>\n      <td>54.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>17.0</td>\n      <td>22.0</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2015/4/13</td>\n      <td>Nhil</td>\n      <td>10.2</td>\n      <td>24.7</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>E</td>\n      <td>39.0</td>\n      <td>E</td>\n      <td>...</td>\n      <td>63.0</td>\n      <td>33.0</td>\n      <td>1021.9</td>\n      <td>1017.9</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.5</td>\n      <td>23.7</td>\n      <td>No</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010/8/5</td>\n      <td>Nuriootpa</td>\n      <td>-0.4</td>\n      <td>11.0</td>\n      <td>3.6</td>\n      <td>0.4</td>\n      <td>1.6</td>\n      <td>W</td>\n      <td>28.0</td>\n      <td>N</td>\n      <td>...</td>\n      <td>97.0</td>\n      <td>78.0</td>\n      <td>1025.9</td>\n      <td>1025.3</td>\n      <td>7.0</td>\n      <td>8.0</td>\n      <td>3.9</td>\n      <td>9.0</td>\n      <td>Yes</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013/3/18</td>\n      <td>Adelaide</td>\n      <td>13.2</td>\n      <td>22.6</td>\n      <td>0.0</td>\n      <td>15.4</td>\n      <td>11.0</td>\n      <td>SE</td>\n      <td>44.0</td>\n      <td>E</td>\n      <td>...</td>\n      <td>47.0</td>\n      <td>34.0</td>\n      <td>1025.0</td>\n      <td>1022.2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>15.2</td>\n      <td>21.7</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011/2/16</td>\n      <td>Sale</td>\n      <td>14.1</td>\n      <td>28.6</td>\n      <td>0.0</td>\n      <td>6.6</td>\n      <td>6.7</td>\n      <td>E</td>\n      <td>28.0</td>\n      <td>NE</td>\n      <td>...</td>\n      <td>92.0</td>\n      <td>42.0</td>\n      <td>1018.0</td>\n      <td>1014.1</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>19.1</td>\n      <td>28.2</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 进行简单的数据查看，我们可以利用 .head() 头部.tail()尾部\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6450ef1e-1820-491d-a8f8-521457f10cbd",
   "metadata": {},
   "source": [
    "这里我们发现数据集中存在NaN，一般的我们认为NaN在数据集中代表了缺失值，可能是数据采集或处理时产生的一种错误。这里我们采用-1将缺失值进行填补，还有其他例如“中位数填补、平均数填补”的缺失值处理方法有兴趣的同学也可以尝试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21e025be-56a2-44e8-8adb-5c359d5fb963",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a494546-d012-4fda-a853-0cbbecd2166b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "              Date      Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n106639   2011/5/23    Launceston     10.1     16.1      15.8         -1.0   \n106640   2014/12/9     GoldCoast     19.3     31.7      36.0         -1.0   \n106641   2014/10/7    Wollongong     17.5     22.2       1.2         -1.0   \n106642   2012/1/16     Newcastle     17.6     27.0       3.0         -1.0   \n106643  2014/10/21  AliceSprings     16.3     37.9       0.0         14.2   \n\n        Sunshine WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  \\\n106639      -1.0          SE           31.0        NNW  ...        99.0   \n106640      -1.0          SE           80.0        NNW  ...        75.0   \n106641      -1.0         WNW           65.0        WNW  ...        61.0   \n106642      -1.0          -1           -1.0         NE  ...        68.0   \n106643      12.2         ESE           41.0        NNE  ...         8.0   \n\n        Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n106639         86.0        999.2        995.2      -1.0      -1.0     13.0   \n106640         76.0       1013.8       1010.0      -1.0      -1.0     26.0   \n106641         56.0       1008.2       1008.2      -1.0      -1.0     17.8   \n106642         88.0         -1.0         -1.0       6.0       5.0     22.6   \n106643          6.0       1017.9       1014.0       0.0       1.0     32.2   \n\n        Temp3pm  RainToday  RainTomorrow  \n106639     15.6        Yes           Yes  \n106640     25.8        Yes           Yes  \n106641     21.4        Yes            No  \n106642     26.4        Yes            No  \n106643     35.7         No            No  \n\n[5 rows x 23 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Location</th>\n      <th>MinTemp</th>\n      <th>MaxTemp</th>\n      <th>Rainfall</th>\n      <th>Evaporation</th>\n      <th>Sunshine</th>\n      <th>WindGustDir</th>\n      <th>WindGustSpeed</th>\n      <th>WindDir9am</th>\n      <th>...</th>\n      <th>Humidity9am</th>\n      <th>Humidity3pm</th>\n      <th>Pressure9am</th>\n      <th>Pressure3pm</th>\n      <th>Cloud9am</th>\n      <th>Cloud3pm</th>\n      <th>Temp9am</th>\n      <th>Temp3pm</th>\n      <th>RainToday</th>\n      <th>RainTomorrow</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>106639</th>\n      <td>2011/5/23</td>\n      <td>Launceston</td>\n      <td>10.1</td>\n      <td>16.1</td>\n      <td>15.8</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>SE</td>\n      <td>31.0</td>\n      <td>NNW</td>\n      <td>...</td>\n      <td>99.0</td>\n      <td>86.0</td>\n      <td>999.2</td>\n      <td>995.2</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>13.0</td>\n      <td>15.6</td>\n      <td>Yes</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>106640</th>\n      <td>2014/12/9</td>\n      <td>GoldCoast</td>\n      <td>19.3</td>\n      <td>31.7</td>\n      <td>36.0</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>SE</td>\n      <td>80.0</td>\n      <td>NNW</td>\n      <td>...</td>\n      <td>75.0</td>\n      <td>76.0</td>\n      <td>1013.8</td>\n      <td>1010.0</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>26.0</td>\n      <td>25.8</td>\n      <td>Yes</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>106641</th>\n      <td>2014/10/7</td>\n      <td>Wollongong</td>\n      <td>17.5</td>\n      <td>22.2</td>\n      <td>1.2</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>WNW</td>\n      <td>65.0</td>\n      <td>WNW</td>\n      <td>...</td>\n      <td>61.0</td>\n      <td>56.0</td>\n      <td>1008.2</td>\n      <td>1008.2</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>17.8</td>\n      <td>21.4</td>\n      <td>Yes</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>106642</th>\n      <td>2012/1/16</td>\n      <td>Newcastle</td>\n      <td>17.6</td>\n      <td>27.0</td>\n      <td>3.0</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>-1</td>\n      <td>-1.0</td>\n      <td>NE</td>\n      <td>...</td>\n      <td>68.0</td>\n      <td>88.0</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>22.6</td>\n      <td>26.4</td>\n      <td>Yes</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>106643</th>\n      <td>2014/10/21</td>\n      <td>AliceSprings</td>\n      <td>16.3</td>\n      <td>37.9</td>\n      <td>0.0</td>\n      <td>14.2</td>\n      <td>12.2</td>\n      <td>ESE</td>\n      <td>41.0</td>\n      <td>NNE</td>\n      <td>...</td>\n      <td>8.0</td>\n      <td>6.0</td>\n      <td>1017.9</td>\n      <td>1014.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>32.2</td>\n      <td>35.7</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32900416-c194-4382-b19b-ccd8cef7fc8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "No     82786\nYes    23858\nName: RainTomorrow, dtype: int64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 利用value_counts函数查看训练集标签的数量\n",
    "pd.Series(data['RainTomorrow']).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428c064a-d87d-4b77-a5b2-64d60f72ebb6",
   "metadata": {},
   "source": [
    "我们发现数据集中的负样本数量远大于正样本数量，这种常见的问题叫做“数据不平衡”问题，在某些情况下需要进行一些特殊处理。`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e9b0727-dfcd-4519-883a-80137814fa6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "             MinTemp        MaxTemp       Rainfall    Evaporation  \\\ncount  106644.000000  106644.000000  106644.000000  106644.000000   \nmean       12.129147      23.183398       2.313912       2.704798   \nstd         6.444358       7.208596       8.379145       4.519172   \nmin        -8.500000      -4.800000      -1.000000      -1.000000   \n25%         7.500000      17.900000       0.000000      -1.000000   \n50%        12.000000      22.600000       0.000000       1.600000   \n75%        16.800000      28.300000       0.600000       5.400000   \nmax        31.900000      48.100000     268.600000     145.000000   \n\n            Sunshine  WindGustSpeed   WindSpeed9am   WindSpeed3pm  \\\ncount  106644.000000  106644.000000  106644.000000  106644.000000   \nmean        3.509008      37.305137      13.852200      18.265378   \nstd         5.105696      16.585310       8.949659       9.118835   \nmin        -1.000000      -1.000000      -1.000000      -1.000000   \n25%        -1.000000      30.000000       7.000000      11.000000   \n50%         0.200000      37.000000      13.000000      17.000000   \n75%         8.700000      46.000000      19.000000      24.000000   \nmax        14.500000     135.000000     130.000000      87.000000   \n\n         Humidity9am    Humidity3pm    Pressure9am    Pressure3pm  \\\ncount  106644.000000  106644.000000  106644.000000  106644.000000   \nmean       67.940353      50.104657     917.003689     914.995385   \nstd        20.481579      22.136917     304.042528     303.120731   \nmin        -1.000000      -1.000000      -1.000000      -1.000000   \n25%        56.000000      35.000000    1011.000000    1008.500000   \n50%        70.000000      51.000000    1016.700000    1014.200000   \n75%        83.000000      65.000000    1021.800000    1019.400000   \nmax       100.000000     100.000000    1041.000000    1039.600000   \n\n            Cloud9am       Cloud3pm        Temp9am        Temp3pm  \ncount  106644.000000  106644.000000  106644.000000  106644.000000  \nmean        2.381231       2.285670      16.877842      21.257600  \nstd         3.483751       3.419658       6.629811       7.549532  \nmin        -1.000000      -1.000000      -7.200000      -5.400000  \n25%        -1.000000      -1.000000      12.200000      16.300000  \n50%         1.000000       1.000000      16.700000      20.900000  \n75%         6.000000       6.000000      21.500000      26.300000  \nmax         9.000000       9.000000      39.400000      46.200000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MinTemp</th>\n      <th>MaxTemp</th>\n      <th>Rainfall</th>\n      <th>Evaporation</th>\n      <th>Sunshine</th>\n      <th>WindGustSpeed</th>\n      <th>WindSpeed9am</th>\n      <th>WindSpeed3pm</th>\n      <th>Humidity9am</th>\n      <th>Humidity3pm</th>\n      <th>Pressure9am</th>\n      <th>Pressure3pm</th>\n      <th>Cloud9am</th>\n      <th>Cloud3pm</th>\n      <th>Temp9am</th>\n      <th>Temp3pm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n      <td>106644.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>12.129147</td>\n      <td>23.183398</td>\n      <td>2.313912</td>\n      <td>2.704798</td>\n      <td>3.509008</td>\n      <td>37.305137</td>\n      <td>13.852200</td>\n      <td>18.265378</td>\n      <td>67.940353</td>\n      <td>50.104657</td>\n      <td>917.003689</td>\n      <td>914.995385</td>\n      <td>2.381231</td>\n      <td>2.285670</td>\n      <td>16.877842</td>\n      <td>21.257600</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>6.444358</td>\n      <td>7.208596</td>\n      <td>8.379145</td>\n      <td>4.519172</td>\n      <td>5.105696</td>\n      <td>16.585310</td>\n      <td>8.949659</td>\n      <td>9.118835</td>\n      <td>20.481579</td>\n      <td>22.136917</td>\n      <td>304.042528</td>\n      <td>303.120731</td>\n      <td>3.483751</td>\n      <td>3.419658</td>\n      <td>6.629811</td>\n      <td>7.549532</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-8.500000</td>\n      <td>-4.800000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-7.200000</td>\n      <td>-5.400000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>7.500000</td>\n      <td>17.900000</td>\n      <td>0.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>30.000000</td>\n      <td>7.000000</td>\n      <td>11.000000</td>\n      <td>56.000000</td>\n      <td>35.000000</td>\n      <td>1011.000000</td>\n      <td>1008.500000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>12.200000</td>\n      <td>16.300000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>12.000000</td>\n      <td>22.600000</td>\n      <td>0.000000</td>\n      <td>1.600000</td>\n      <td>0.200000</td>\n      <td>37.000000</td>\n      <td>13.000000</td>\n      <td>17.000000</td>\n      <td>70.000000</td>\n      <td>51.000000</td>\n      <td>1016.700000</td>\n      <td>1014.200000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>16.700000</td>\n      <td>20.900000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>16.800000</td>\n      <td>28.300000</td>\n      <td>0.600000</td>\n      <td>5.400000</td>\n      <td>8.700000</td>\n      <td>46.000000</td>\n      <td>19.000000</td>\n      <td>24.000000</td>\n      <td>83.000000</td>\n      <td>65.000000</td>\n      <td>1021.800000</td>\n      <td>1019.400000</td>\n      <td>6.000000</td>\n      <td>6.000000</td>\n      <td>21.500000</td>\n      <td>26.300000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>31.900000</td>\n      <td>48.100000</td>\n      <td>268.600000</td>\n      <td>145.000000</td>\n      <td>14.500000</td>\n      <td>135.000000</td>\n      <td>130.000000</td>\n      <td>87.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>1041.000000</td>\n      <td>1039.600000</td>\n      <td>9.000000</td>\n      <td>9.000000</td>\n      <td>39.400000</td>\n      <td>46.200000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 对于特征进行一些统计描述\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602749eb-8d04-4b36-a9eb-c8a9f241a37b",
   "metadata": {},
   "source": [
    "# Step4:可视化描述"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f331f296-cb5a-4d21-98da-d0ef2769045b",
   "metadata": {},
   "source": [
    "为了方便，我们先纪录数字特征与非数字特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b23b590e-a30e-4c78-89ff-4a895fc95943",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/td/.local/miniconda3/envs/py37_ml/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "numerical_features = [x for x in data.columns if data[x].dtype == np.float]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ec051f0e-31b7-4ec7-a1fe-c8939dc08f6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/td/.local/miniconda3/envs/py37_ml/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "category_features = [x for x in data.columns if data[x].dtype != np.float and x != 'RainTomorrow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "387b9cde-d314-438d-9d98-36fe5a0b4b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 617.875x540 with 12 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 选取三个特征与标签组合的散点可视化\n",
    "sns.pairplot(data=data[['Rainfall',\n",
    "'Evaporation',\n",
    "'Sunshine'] + ['RainTomorrow']], diag_kind='hist', hue= 'RainTomorrow')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e61230e-3399-4643-848d-b6a76d7fc12d",
   "metadata": {},
   "source": [
    "从上图可以发现，在2D情况下不同的特征组合对于第二天下雨与不下雨的散点分布，以及大概的区分能力。相对的Sunshine与其他特征的组合更具有区分能力"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c93890b5-1416-4de8-8962-e5f8612e5438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYuElEQVR4nO3de5QfZZ3n8fcnDRIwaTohPYgdMWS5OF5zaTIZFWcMyQ6SWZqNjIjKRGXMmYkz6jhZYYajggzC7Dii7m7GzSxoVG7K4IaLR7cBGfEgJumAXEQhdLikgaRDEtLBEKDz3T+qOvy605dfkq5fdffzeZ3Tp+up+lXVl9h++umqp55SRGBmZukYV3YBZmZWWw5+M7PEOPjNzBLj4DczS4yD38wsMQ5+M7PEOPjNzBLj4LcxR9Ljkl6SNKXP+nslhaRpB3jcYyXtrPgKSS9UtE8Zlv8As4I5+G2s2gCc09OQ9DbgiIM5YEQ8GRETer7y1e+oWHfXwRzfrFYc/DZWfRf484r2YuA7PQ1JC/O/AHZIekrSRRXbzpa0QVJ93n6fpGclNQ50MkmHSfqKpCclbZL0TUmH59v+WNJGSZ+TtFnSM5LOlHS6pEckbZX0DxXHukjSDZKul9QlaZ2kdwzfP42lzsFvY9U9QL2k35dUB3wQ+F7F9hfIfjE0AAuBv5J0JkBEXA/cDXxD0lHAlcBfRETnIOe7HDgRmAEcDzQBX6jY/jpgfMX6fwM+AswGTgE+L+m4is+3AD8AJgPXAP9X0qH79S9gNgAHv41lPb3+BcDDQEfPhoi4MyIeiIg9EXE/cC3wRxX7fhKYB9wJ3BwRtwx0EkkClgB/GxFbI6IL+DLZL5seLwOXRsTLwHXAFODrEdEVEQ8BvwYqe/VtEXFD/vmvkv3SmHsg/whmfR1SdgFmBfou8DPgOCou8wBI+gOyXvpbgdcAh5H1sAGIiO2SfgB8Fnj/EOdpJLt/0Jb9DshOAdRVfOa5iOjOl3fl3zdVbN8FTKhoP1VRyx5JG4HXD1GHWVXc47cxKyKeILvJezpwY5/N1wA3AW+IiCOBb5KFNQCSZgAfJ/tL4BtDnGoLWXC/JSIa8q8jK24AH4g3VNQyDpgKPH0QxzPby8FvY915wLyIeKHP+onA1oh4UdIc4EM9GySNJ7sf8A/Ax4AmSUsHOkFE7CG7Zn+FpN/Lj9Ek6U8Oou7ZkhZJOgT4DLCb7L6F2UFz8NuYFhGPRcTafjYtBb4kqYvsZuv3K7ZdBjwVEf8aEbvJbsL+o6QTBjnV+cB64B5JO4DbgJMOovRVwNnANuBcYFF+vd/soMkvYjEbWfKhpcdHxEfKrsXGJvf4zcwS4+A3M0uML/WYmSXGPX4zs8QU+gCXpMeBLqAbeCUimiVNBq4HpgGPAx+IiG2DHWfKlCkxbdq0Iks1Mxtz2tratkTEPnNM1eLJ3fdGxJaK9gXA7RFxuaQL8vb5gx1g2rRprF3b34g8MzMbiKQn+ltfxqWeFmBlvrwSOLOEGszMklV08Afw/yS1SVqSrzs6Ip7Jl58Fju5vR0lLJK2VtLazc7BJEc3MbH8Ufann3RHRkT/G3irpN5UbIyIk9TusKCJWACsAmpubPfTIzGyYFNrjj4iO/Ptm4IfAHGCTpGMA8u+bi6zBzMx6Kyz4Jb1W0sSeZeA/Aw+SzYi4OP/YYrI5SaxGduzYwfLly9mxY0fZpZhZSYrs8R8N/FzSr4DVwK0R8WOyOdAXSHoUmJ+3rUZaW1vZsGEDt912W9mlmFlJCrvGHxHt9H6jUM/654BTizqvDWzHjh2sWbOGiGDNmjXMnz+f+vr6sssysxrzk7sJaW1tpWeKjj179rjXb5YoB39C1q1bR3d39va/7u5u2traSq7IzMrg4E/IrFmzerVnz55dUiVm+/LAg9px8Cdk7ty5g7bNyuSBB7Xj4E/IzTff3Kt9yy23lFSJWW+VAw9Wr17tXn/BHPwJWb9+fa/2I488UlIlZr21trb2uv/kXn+xHPxmVrq2tra9I84iwrPxFszBb2almzRp0qBtG14OfjMr3bZt2wZt2/By8JtZ6foOLW5ubi6pkjQ4+M2sdAsWLOjVnj9/fkmVpMHBb2al6+rq6tXeuXNnSZWkwcFvZqW75pprerWvvvrqkipJg4PfzEq3adOmQds2vBz8Zla6cePGDdq24VX0O3ctt2rVKjo6OsouYx/Lly8v5bxNTU20tLSUcm4befbs2TNo24aXf62amSXGPf4aGQm920suuYTnn39+b3vSpEksXbq0xIrMrAzu8Sfk85//fK/2hRdeWFIlZr31vaZfV1dXUiVpcPAnynOh2EjS95p+z0ydVgxf6knM9OnTAXyJxyxh7vGbmSXGwW9mlhgHv5lZYhz8ZmaJcfCbmSXGwW9mlhgHv5lZYgofxy+pDlgLdETEn0o6DrgOOApoA86NiJeKrsPM+ucJBHtLYQLBWvT4Pw08XNH+J+CKiDge2AacV4MazMwsV2iPX9JUYCFwKfBZSQLmAR/KP7ISuAj41yLrMLOBjYTe7bJly/ZZ56fLi1N0j/9rwOeAnok4jgK2R8QreXsj0FRwDWY2wi1atKhX+6yzziqpkjQUFvyS/hTYHBFtB7j/EklrJa3t7Owc5urMbCR55zvf2as9d+7ckipJQ5E9/ncBZ0h6nOxm7jzg60CDpJ5LTFOBfu8qRcSKiGiOiObGxsYCyzSzkWDy5MmAe/u1UFjwR8TfR8TUiJgGfBC4IyI+DPwU6PlfdjGwqqgazGz0aGhoYPr06e7t10AZ4/jPJ7vRu57smv+VJdRgZpasmszHHxF3Anfmy+3AnFqc18zM9uUnd83MEuPgNzNLjIPfzCwxDn4zs8Q4+M3MEuPgNzNLjIPfzCwxDn4zs8Q4+M3MEuPgNzNLjIPfzCwxDn4zs8Q4+M3MEuPgNzNLjIPfzCwxDn4zs8Q4+M3MEuPgNzNLjIPfzCwxDn4zs8Q4+M3MEuPgNzNLjIPfzCwxDn4zs8Q4+M3MEuPgNzNLjIPfzCwxDn4zs8Q4+M3MElNY8EsaL2m1pF9JekjSxfn64yT9UtJ6SddLek1RNZiZ2b6K7PHvBuZFxDuAGcBpkuYC/wRcERHHA9uA8wqswczM+igs+COzM28emn8FMA+4IV+/EjizqBrMzGxfhV7jl1Qn6T5gM9AKPAZsj4hX8o9sBJoG2HeJpLWS1nZ2dhZZpplZUgoN/ojojogZwFRgDvCm/dh3RUQ0R0RzY2NjUSWamSWnJqN6ImI78FPgD4EGSYfkm6YCHbWowczMMocM/ZEDI6kReDkitks6HFhAdmP3p8BZwHXAYmBVUTX0WLVqFR0d/v0C8PTTTwOwfPnykispX1NTEy0tLWWXYVZzhQU/cAywUlId2V8W34+IWyT9GrhO0j8C9wJXFlgDAB0dHTz51EYmNEwp+lQj3h7VAbC168WSKynXzu1byi7BrDSFBX9E3A/M7Gd9O9n1/pqa0DCF2fMW1fq0NkK13XFj2SWYlcZP7pqZJcbBb2aWGAe/mVliHPxmZomp+uaupNeR3ZQNYE1EPFtYVWZmVpiqevyS/gJYDSwiG4N/j6SPF1mYmZkVo9oe/38DZkbEcwCSjgLuBq4qqjAzMytGtdf4nwO6Ktpd+TozMxtlqu3xrwd+KWkV2TX+FuB+SZ8FiIivFlSf2Zjm6URe5elEeitySpFqg/+x/KtHz/w6E4e3HLO0dHR0sPHJx2mceHjZpZSuLp+tffe2TSVXUr7Orl2FHr+q4I+IiwutwixhjRMPZ9Gc/1R2GTaC3Lj6saE/dBCqCn5JzcCFwBsr94mItxdUl5mZFaTaSz1Xk43seQDYU1w5ZmZWtGqDvzMibiq0EjMzq4lqg/+Lkv4PcDuwu2dlRHhuWzOzUaba4P8Y2ftyD+XVSz0BOPjNzEaZaoP/5Ig4qdBKzMysJqp9cvduSW8utBIzM6uJanv8c4H7JG0gu8YvIEbLcM4tW7bwwq4X/bo926tr+xb27B5fdhlmpag2+E8rtAozM6uZap/cfULSu4ETIuJbkhqBCcWWNnymTJnCuK4X/bJ126vtjhuZPNE9fktTtfPxfxE4H/j7fNWhwPeKKsrMzIpT7c3d/wqcAbwAEBFP4wnazMxGpWqD/6WICLKx+0h6bXElmZlZkQYNfklfzhe/L+l/Aw2SPgHcBvxb0cWZmdnwG6rHfxpARHwFuAH4d+Ak4AsR8T8Krs3MzAow1KieOkmTyMbtt+VfAEiaHBFbiyzOzMyG31DB/yaysBf59f1cT3t6QXWZmVlBhgr+X0fEzAM5sKQ3AN8Bjib7JbEiIr4uaTJwPTANeBz4QERsO5BzmJnZ/qv2yd0D8QrwdxGxTtJEoE1SK/BR4PaIuFzSBcAFZM8ImCVny5YtvPi7XYW/as9Gl86uXYzv3lLY8Ye6uft1AEn7POIoacpgO0bEMxGxLl/uAh4GmoAWYGX+sZXAmftXspmZHYxBe/wR8e18cY2kT0TEPQCS3g9cBpxYzUkkTQNmAr8Ejo6IZ/JNz5JdCupvnyXAEoBjjz22mtOYjTpTpkxh97Zuv2zderlx9WMcNmnQvvVBqfZSz4eAqyTdCbweOAqYV82OkiaQDQP9TETskLR3W0SEpOhvv4hYAawAaG5u7vczZma2/6qdpO0BSZcC3wW6gPdExMah9pN0KFnoX13xmsZNko6JiGckHQNsPsDazczsAFQ7SduVwGeAt5O9hvEWSZ8cYh8BVwIPR8RXKzbdBCzOlxcDq/azZjMzOwjVztXzAPDeiNgQET8B/gCYNcQ+7wLOBeZJui//Oh24HFgg6VFgft42M7MaqfZSz9f6tJ8Hzhtin5+TPejVn1OrOe9w2rl9i9/ABfxu5/MAHDHhyJIrKdfO7VuYPHFq2WWYlaKq4Jd0AtkonjcDe4d2RsSoeHK3qamp7BJGjBe7slk2Un8JyeSJU/1zYcmqdlTPt4AvAlcA7yW7zl/tZaLStbS0lF3CiLF8+XIAli5dWnIlZlaWasP78Ii4HVBEPBERFwELiyvLzMyKUm2Pf7ekccCjkv4a6GAUvXPXzMxeVW2P/9PAEcCngNlko3UWD7qHmZmNSNWO6lmTL+4ku75vZmaj1KDBL+mmwbZHxBnDW46ZmRVtqB7/HwJPAdeSTbA20Lh8MzMbJYYK/tcBC4BzyCZquxW4NiIeKrows1R0dnk+foDtv9sNQMMRh5VcSfk6u3YxdVJxxx9qWuZu4MfAjyUdRvYL4E5JF0fE/yyuLLM0+CGyV3XvehqAwyb1O1N7UqZOKvZnY8ibu3ngLyQL/WnAN4AfFlaRWUL8cOGr/HBh7Qx1c/c7wFuBHwEXR8SDNanKzMwKM1SP/yPAC2Tj+D9V8RIVkb1Hpb7A2szMrABDXeMfNfPxmJlZdRzsZmaJcfCbmSXGwW9mlhgHv5lZYhz8ZmaJcfCbmSXGwW9mlhgHv5lZYhz8ZmaJcfCbmSXGwW9mlhgHv5lZYhz8ZmaJcfCbmSWmsOCXdJWkzZIerFg3WVKrpEfz7wW+VdLMzPpTZI//28BpfdZdANweEScAt+dtMzOrocKCPyJ+Bmzts7oFWJkvrwTOLOr8ZmbWv1pf4z86Ip7Jl58Fjq7x+c3Mklfazd2ICCAG2i5piaS1ktZ2dnbWsDIzs7Gt1sG/SdIxAPn3zQN9MCJWRERzRDQ3NjbWrEAzs7Gu1sF/E7A4X14MrKrx+c3MklfkcM5rgV8AJ0naKOk84HJggaRHgfl528zMauiQog4cEecMsOnUos5pZmZD85O7ZmaJcfCbmSXGwW9mlhgHv5lZYhz8ZmaJcfCbmSXGwW9mlhgHv5lZYhz8ZmaJcfCbmSXGwW9mlhgHv5lZYhz8ZmaJcfCbmSXGwW9mlhgHv5lZYhz8ZmaJcfAnpr29nfb2di699NKySzGzkjj4E7Vt27aySzCzkhT2zl3rbdWqVXR0dJRaQ3t7e6/2smXLmD59eim1NDU10dLSUsq5zVLnHr+ZWWLc46+RkdC7XbZs2T7rli5dWkIlZlYm9/jNzBLj4DczS4yD38xGhO3bt9Pe3s4999xTdiljnoPfzEaErVu3AnDDDTeUXMnY55u7ZokbCUONt2/f3qt92WWXceSRR5ZSSwpDjd3jN7PS9fT2ezz33HMlVZIG9/jNEjcSercealxbpfT4JZ0m6beS1ku6oIwazMxSVfPgl1QH/C/gfcCbgXMkvbnWdZiZpaqMHv8cYH1EtEfES8B1QPl/a5qZJaKM4G8Cnqpob8zX9SJpiaS1ktZ2dnbWrDgzs7FuxI7qiYgVEdEcEc2NjY1ll2NmNmaUEfwdwBsq2lPzdWZmVgNlBP8a4ARJx0l6DfBB4KYS6jAzS1LNx/FHxCuS/hr4CVAHXBURD9W6DjOzVJXyAFdE/Aj4URnnNjNL3Yi9uWtmZsVw8JuZJcbBb2aWGAd/QiQN2jazNDj4EzJr1qxe7dmzZ5dUiZmVycGfkIULF/Zqn3766SVVYmZlcvAnpL6+fm8vv7m5mfr6+pIrMrMy+EUsiVm4cCHbtm1zb99GlIaGhl6vX2xoaCitlhS4x29mpduxY8egbRteDv7EtLa2smHDBm677baySzHbyyPOasvBn5AdO3awZs0aIoI1a9a4V2UjxowZM3q1Z86cWU4hiXDwJ6S1tZWIAGDPnj3u9duIsXDhwr29/HHjxvkeVMEc/AlZt24d3d3dAHR3d9PW1lZyRWaZ+vr6vc+ZzJo1yyPOCubgT8isWbOoq6sDoK6uzg9w2YiycOFCpk+f7t5+DTj4E7JgwYJef07Pnz+/5IrMXlVfX8/SpUvd268BB39C6uvrOfnkk5HEySef7P+DmSXKD3AlZsGCBWzatMm9fbOEOfgT0/PntJmly5d6zMwS4+A3M0uMg9/MLDEOfjOzxKjnEf6RTFIn8ETZdYwhU4AtZRdh1g//bA6vN0ZEY9+VoyL4bXhJWhsRzWXXYdaXfzZrw5d6zMwS4+A3M0uMgz9NK8ouwGwA/tmsAV/jNzNLjHv8ZmaJcfCbmSXGwT+GSQpJ/1LRXibpohJLssQp83NJ76tY92eSflxmXalx8I9tu4FFkqaUXYgZQGQ3Ff8S+Kqk8ZImAF8GPlluZWlx8I9tr5CNkvjbvhskTZN0h6T7Jd0u6djal2cpiogHgZuB84EvAN8DLpS0WtK9kloAJL0lX3df/nN6Qolljyke1TOGSdoJvB64H3gH8AlgQkRcJOlm4IaIWCnp48AZEXFmedVaSiS9FlgHvATcAjwUEd+T1ACsBmYClwP3RMTVkl4D1EXErrJqHksc/GOYpJ0RMUHSl4CXgV28GvxbgGMi4mVJhwLPRIQvCVnN5D+XO4EPAOPJ/kIFmAz8CVn4Xwh8B7gxIh4to86xyJd60vA14DzgtSXXYVZpT/4l4P0RMSP/OjYiHo6Ia4AzyDosP5I0r8xixxIHfwIiYivwfbLw73E38MF8+cPAXbWuyyz3E+BvJAlA0sz8+3SgPSK+AawC3l5eiWOLgz8d/0I25W2PvwE+Jul+4Fzg06VUZQaXAIcC90t6KG9DdgnoQUn3AW8lu+Rjw8DX+M3MEuMev5lZYhz8ZmaJcfCbmSXGwW9mlhgHv5lZYg4puwCzAyGpG3iA7Gd4A3BuRGwf5PPNwJ9HxKcG2H4UcHvefB3QDXTm7TkR8dIwlW5WOg/ntFGpZzqKfHkl8EhEXDpMx74I2BkRXxmO4w1xrkMi4pWB2mZF8KUeGwt+ATQBSJoj6Rf5LI93SzopX//Hkm7Jly+SdJWkOyW1S+r3r4D8s6fmx3og3+ewfP3jki7LZ45cK2mWpJ9IekzSX+afkaR/lvRgvv/ZFbXcJekm4Nf9tMdL+la+z72S3pvvd6ukt+fL90r6Qr78JUmfKOjf1sYgB7+NapLqgFOBm/JVvwFOiYiZZFP+fnmAXd9ENhHYHOCL+UR1fY89Hvg2cHZEvI3sstJfVXzkyYiYQTbdxbeBs4C5wMX59kXADLKZUecD/yzpmHzbLODTEXFiP+1Pkk1d/zbgHGBlXstdwCmSjiSb0Oxd+b6nAD8b8B/JrA8Hv41Wh+eP8j8LHA205uuPBH4g6UHgCuAtA+x/a0TsjogtwOb8GH2dBGyIiEfy9krgPRXbe37ZPAD8MiK6IqIT2J1PL/xu4NqI6I6ITcB/ACfn+6yOiA0Vx6psv5tsjnoi4jfAE8CJZMH/HrLAvxWYIOkI4LiI+O0A/51m+3Dw22i1K+9tv5FsdseeNzhdAvw0It4K/Bey6X77s7tiuZsDG+jQc4w9fY63p4rjvTBEuz9rgGZe7eHfS/aOhbYq9jXby8Fvo1pE/A74FPB3kg4h6/F35Js/epCH/y0wTdLxeftcsl57te4CzpZUJ6mRrLe+usr9Pgwg6UTgWOC3+ciip4A/I7uvcRewDF/msf3k4LdRLyLuJXvL2DnAfwcuk3QvBzlcOSJeBD5GdunoAbKe/Df34xA/zOv6FXAH8LmIeLaK/ZYD4/JzXg98NCJ6/qK4C9icv4nqLmAqnlLb9pOHc5qZJcY9fjOzxDj4zcwS4+A3M0uMg9/MLDEOfjOzxDj4zcwS4+A3M0vM/werqAyeTk1g3wAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in data[numerical_features].columns:\n",
    "    if col != 'RainTomorrow':\n",
    "        sns.boxplot(x='RainTomorrow', y=col, saturation=0.5, palette='pastel', data=data)\n",
    "        plt.title(col)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549de707-32e7-460a-bc0e-554887b001f3",
   "metadata": {},
   "source": [
    "利用箱型图我们也可以得到不同类别在不同特征上的分布差异情况。我们可以发现Sunshine,Humidity3pm,Cloud9am,Cloud3pm的区分能力较强"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "822f0c39-356b-4d77-a4b0-b19effeca0ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "tlog = {}\n",
    "for i in category_features:\n",
    "    tlog[i] = data[data['RainTomorrow'] == 'Yes'][i].value_counts()\n",
    "flog = {}\n",
    "for i in category_features:\n",
    "    flog[i] = data[data['RainTomorrow'] == 'No'][i].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98f5ae26-0b64-4a21-8761-efe8f06a7f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x720 with 2 Axes>",
      "image/png": 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JMyXdlNpcl+w51IUAEfFhRDyfjr9G0mWSpkv6h6QD0vaxku6U9CDwQL2Z1bGSbpV0T4p0+1VeH7+W2pkq6XJJl6Tth0ual2YjHumwX6KZdZnFixfz6KOPcuWVV3LDDTd8bP+kSZM44IAD6o497rjjGDRoENXV1dxyyy25w9ZzDTOzUlMoXwZqSZRazlnAuxExCEDSJyT1BX4E7B0RSyR9F/jfiPiJpDuBlyU9ANwFTIyIlamtKrKItq2BhyR9Om0fClRHxNvKItjy1ZDNKnwIPJ8elq9N/RoKvA88CMxOx/8Y2C8iXpW0QWO/ADmC0qxo3XHHHXzuc59j2223pU+fPsyYMYM+fRpOyPnpT3/K+uuvz9y5cwF45513WLBgAcAmwJBirGFyBKWZNaIgZjRpWZRazt7kpV9ExDvATsAOwGOSZgHHAluk/V8H9iJbM+504Kq8tv4vIlZGxAvAi8D2afv9EfF2I9d/ICLejYhlwDPpOiOBhyPi7ZQDfFPe8Y8B10g6gSy9o0GOoDQrXhMnTuSII7KbMUcccUSDt89z/v73v3PyySfXvf/EJz7BE088AVlCT1HWMEdQmlljunxGUy2PUmuyGbLC2uAANSLmAnMlXUeWnjE2t6v+oennEhr3Yd7rWpr5HUbESZJ2BL4AzJA0LCIam601syLz9ttv8+CDDzJ37lwkUVtbi6TVBpPNSat/vBcRNY3sdw0zs6JUCDOaLY1Sy7kfqKvgkj4BPAHskrttJKmXpG3T85yj886tAV7Oe3+4pG6Stga2Ap5v42eYBnw23cbvDhya17+tI+LJiPgx8FYTn8vMitDNN9/M0Ucfzcsvv8z8+fN55ZVX2HLLLXnllVcaPH6fffbh0ktX/R39zjvvsNNOOwH0dg0zs1LT5TOatDxKLednwKXp4fZa4JyIuFXSWGCisuxeyJ7ZfB04U9IfgaVkf+WPzWvr32S3o9YDToqIZZJa/QHSs0s/T229TRbp9m7afb6kbchmXR9g1XNPjXIEpVnxmDhxIt/97ndX23booYfyi1/8osHjf/SjH3HyySczcOBAKioqOPvss/nSl74EMJ8SqGGOoDSzfGW7YLuka4C7IqJd1peT1DsiFqfZgNuAqyLitra05Qg3s/KjVi7YXqg1zPXLrPw0Vb8K4dZ5qRifHuKfR3br//Yu7Y2ZWeu4hplZuyvbGc1CVioRlNY6juwsb62d0SxUjqA0a157xD4Wkk6b0ZRUK2lWWth3pqSdW3n+eEmnt2efWnHt3pL+KOlfkmZImpS+adkebS9uj3bMrGNVVFRQU1PD4MGDGTp0KI8//nirzh8/fjwXXHBBB/Wuaa5hZlaI2vvLQEtzy3NI2g/4BfDZdr5Gs5TFrq1o5WlXkN0u2iYiVkrakmxtzvx2RTYLvLKhBsysuPXs2ZNZs2YBcO+99/L973+fhx9+uNP74RpmZqWiI5/RXA94B+r+0n4gzXLOlXRQ7iBJP1QWe/YosF3e9hHKIiZnSTpfqyLUKtL7aWn/N9L20ZImpxSNZyT9RNJpee2dK+l/JG0i6ZHU7jxJu6WlQXYEfpQrwBHxUkTcrSy+7XlJfyJ7dmkzSWfkXf+cvGt8VVl826w0s7Da4saS+iqLmPtCe/+yzax9vffee3ziE58AstjIvfbai6FDhzJo0CDuuOOOuuPOPfdctt12W3bddVeef37V6kLTpk2jurqampoazjjjDAYOHAhAbW0tZ5xxBiNGjKC6upo//vGPuVPWdQ0zs1LT3jOaPdPD5GuTxantmbYvAw6JiPeUxUU+kYrpULJs85rUl5nAjHTO1cAJETFF0nl51/gaWQTliLQMyGOS7kv7hgIDI+IlZbFrtwIXSuqWrjOSbGmQeyPi3FRE1wH2AGZFRG0jn2sb4NiIeELSvun9SLLlPu6UtDvZ+nJjgF0iYrmk3wNHAX8CkLQRcCfZ/wjur38BOYLSrMstXbqUmpoali1bxuuvv86DDz4IwNprr81tt93Geuutx4IFC9hpp5048MADmTlzJjfccAOzZs1ixYoVDB06lGHDhgFw3HHHcfnllzNq1Ci+973v1V3jyiuvZP3112fatGl8+OGH7LLLLuy777653UVZw+QISjNrREfeOh8F/EnSQLJi9vNUzFYCmwIbAbsBt0XEB+mcO9PPDYB1I2JKavf/AQek1/sC1ZIOS+/XJyuaHwFTI+IlgIiYryw3fUi61lMRsVDSNOAqST2A2yNilppfd+7liHgi7/r7Ak+l973T9auBYcC01F5P4L/pmB5k68+dHBEN3oeLiAnABMi+DNRch8ys/eXfOp8yZQrHHHMM8+bNIyL4wQ9+wCOPPEK3bt149dVXefPNN5k8eTKHHHII66yzDgAHHnggAIsWLeL9999n1KhRAHzlK1/hrrvuAuC+++5jzpw53HxztirRu+++ywsvvJDrQlHWsPz6VVlZ7fplZnU6bMH2NBPZF+gH7J9+Dkt/Kc8nm/VsCwHjIuLe1TZm6Rn1Y9euIPvrf2NSPnBEPJIGvF8gy+/9DVmW72BJFY3MCOS3K+AXEfHH/AMkjQOujYiGFppfQTZTux/Q+Q98mVmrjRo1igULFvDWW2/x17/+lbfeeosZM2bQo0cPqqqqWLZsWZvajQguvvhi9ttvv4Z2u4aZWUnpsGc0JW1Pllu+kGzW8b9pkLkHsEU67BHgYEk9Ja0LfBEgIhYB72vVNyaPyGv6XuCb6a95lMW09WqkG7cBnwNGpPOQtAXwZkRcTlbEh0bEv4DpwDlKf8qn55oaeg7pXuB4Sb3TcZtK+iTZX/uHpddI2jBdC7L84eOB7SV9t4E2zazAPPfcc9TW1tKnTx/effddPvnJT9KjRw8eeughXn45S4Hcfffduf3221m6dCnvv/8+f/nLXwDYYIMNWHfddXnyyScBuOGGG+ra3W+//fjDH/7A8uXLAfjHP/7BkiWNRpO7hplZUeuoZzQh+6v52IiolXQ98BdJc8mK4XMAETFT0o1kkWb/JcvbzfkacLmklWR/Qefi0K4AqoCZqaC+BRzcUGci4iNJDwGL8v7KHw2cIWk5sBg4Jm3/OvBr4J+SlgILgDMaaPM+SZ8BpqR6vhj4akQ8I+lHwH3pearlZJnsL6fzaiUdSfY81PsR8fumf5Vm1tlyz2hCNvN47bXXUlFRwVFHHcUXv/hFBg0axPDhw9l+++0BGDp0KGPGjGHw4MF88pOfZMSIEXVtXXnllZxwwgl069aNz372s6y//voAfP3rX2f+/PkMHTqUiKBfv37cfvvtDfbHNczMil3BLtiuFIeWXn8P2CQi/qeVbXQj+4LR4RHxQnPHFwpHuJkVv8WLF9O7d28AzjvvPF5//XV+97vfNXq8GljwuBhrmOuXWflpqH7lFHIE5RfSEhvzyL409LPWnCxpB+CfwAPFUqDNrHTcfffd1NTUMHDgQCZPnsyPfvSjVp3vGmZmpaBgZzTLmSMorZg5SrNtmpoRKCaOoLRyVmrRki1VrDOa7UJtiMVUiltLD9PPa8e+/KC92jKz0uf6ZWbFruQHmqS1PSNiMPB9sljMDiGpuS9XuVCbWWu4fplZUSuHgWa+FsViNiTNDkxOx9fNLKhe9GXadrukGZKeVpaYgbJ0o55pduL6Dv2UZlaKXL/MrOh02ILtBaRVsZjR+EOr/wX2iYhlkrYBJgK55xHqYuPS++Mj4m1JPclSNm6JiO9JOiWXnFSfHEFpZh9XdPXLEZRmlq8cBpqtjcV8o5F2egCXSKoBaoFt8/ZNzSvSAKdKOiS93ows3m1hU510BKWZNaDo6pcjKM0sXzkMNOusYSzmt4E3gcFkjxzk58/VxXooi8LcGxgVER9ImtRMu2ZmzXL9MrNiVFbPaKplsZiNWR94PSJWAkendho77p1UpLcHdsrbt1wpOtPMrDVcv8ysGJXDjGarYjGb8HvgFknHAPeQNwtQzz3ASZKeBZ4HnsjbNwGYI2lmRBzVto9jZmXE9cvMipoXbC9AjnAzKz+lsmC765dZ+SnrBdvNzMzMrGuUw63zovPR3LnM33LLru6GdRJHNlopmTv3I7bccn5Xd8OsJJRCpGXJzmi2Jbqthe3WxbpJGi7povZo18wsx/XLzEpFKc9o5q8/tx9ZdNtn2/MCETGd7EF8M7P25PplZiWhZGc066mLbgOQdIakaZLmSDonbauS9Kyky1P02n0pGQNJw9LMwmzg5Lx2Rku6K73uJ+n+dO4Vkl5Oa94h6X8lzUv/TuvEz21mxc/1y8yKVikPNHO5vM8BVwA/BZC0L1nSxUigBhiW0jVI2y+NiAHAIuDQtP1qYFxEDG7iemcDD6ZzbwY2T9cbBhwH7Ei2Jt0JkobUP1nSiZKmS5q+sLa27Z/azEpB0dav2tomQ4TMrMyU8kBzaUTURMT2wOfIotsE7Jv+PQXMBLYnK9AAL0XErPR6BlAlaQNgg4h4JG2/rpHr7QrcABAR97BqBmJX4LaIWBIRi4Fbgd3qnxwREyJieEQM7+Osc7NyV7T1q6KiT1s/s5mVoFJ+RrNOveg2Ab+IiD/mHyOpCvgwb1Mt0LPTOmlm1gDXLzMrZqU8o1mnXnTbvcDxknqnfZtK+mRj50bEImCRpF3TpsYSMR4Dvpza3Bf4RNo+GThY0jqSegGHpG1mZs1y/TKzYlbKM5oNRrcB90n6DDAluxPFYuCrZDMAjTkOuEpSAPc1csw5wERJRwNTgDeA9yNipqRrgKnpuCsi4qm2fywzKwOuX2ZWEhxB2U4kVQK1EbFC0ijgD7nlSVrLEW5m5UddGEHp+mVma6Kp+lXKM5qdbXPg/yR1Az4CTuji/piZtZTrl5l1CA8020lEvAB8bNmPtnAEpXUmR2Bae9YvR1CatR9HUBYYtSK2TdLjzbS1uP17aGbWqGGuX2ZWakptRrPZ2DZJ3SNiRUS0S3awmVk7Wen6ZWalpqRmNOupi21LUWuTJd0JPJO2LU4/N5H0SJpJmCepbjFiSb9NkWwPSOqXtp2Q4t9mS7pF0jpp+zWSLpL0uKQXJR2W187HIuPMzJrg+mVmJaHUBpoNxrYlQ4H/iYht653zFeDeNJMwGJiVtvcCpqdItofJItoAbo2IESnO7Vnga3ltbUKWpHEAcB40GxlXR46gNCt33UqhfjmC0szylfKt81FksW0D076pEdHQtx6mka0x1wO4PS/CbSVwY3r9Z7LoNYCBkn4GbAD0JltAOef2iFgJPCNpo7QtPzKOdM42wCN55xERE4AJANWVlV5zyqz8rCyF+lVZWe36ZWZ1Sm1Gs05ETAFysW0ASxo57hFgd+BV4BpJxzTWZPp5DXBKRAwiW+R47bxj8iPglPfzFym3uCYiPh0RV7b285hZ+XD9MrNSUbIDTa0e29bUcVsAb0bE5WS3q4amXd2A3HNKXwEeTa/XBV5PMwiNxbnla1VknJmZ65eZlYpSu3XeYGxbimprzGjgDEnLyeLccjMCS4CRkn4E/BcYk7afBTwJvJV+rttU4xHRWGTcf1v1ycys1HVz/TKzUuMIygLkCDez8tNUhFsxcf0yKz9N1a+SvXVuZmZmZl2r1G6dlwRHUJq1jeM0u54jKK0UlEL0Y6Eo+xlNSRtLukHSvyTNkPRXSfXXqss/vsnoNzOzzuL6ZWaFrqwHmsqebr8NmBQRW0fEMOD7wEaNndNQ9JskzwybWady/TKzYlDWA01gD2B5RFyW2xARs4GnUmzbTElzJR2U258X/bZaLFx6P0nSzZKek3R9+h8Bks6T9EyKcLugkz+jmZUm1y8zK3jl/pfsQGBGA9uXAYdExHuS+gJPSLozPv4V/aHAwIh4SdJoYAgwAHgNeAzYRdKzwCHA9hERkjZoqCOSTgROBOhfUbHGH8zMSl5B1q+Kiv5r/MHMrHSU+4xmYwT8XNIc4O/ApjR8O6p+LNzUiPhPinGbBVQB75IV/islfQn4oKELRsSEiBgeEcP7eKBpZm3XpfWroqJP+30SMyt65T7QfBoY1sD2o8ii34al7OE3WT2qLad+LFx+hFst0D0iVgAjgZuBA4B71rDPZmbg+mVmRaDcB5oPApXptg8AkqqBLYD/RsRySXuk922SotvWj4i/At8GBq9hn83MwPXLzIpAWT+jmZ45OgS4UNJ3yW4RzQfGAxdJmgtMB55bg8usC9whaW2yW1r/u0adNjPD9cvMioMjKAuQI9zMyo8jKM2sWDmC0szMzMw6XVnfOi9UjqC01nDsohUSR1CaFY/OiNr0jKaZmZmZdYiSGWi2NvO3iXYmSSr656TMrHi88cYbAFu5fplZqSmJW+d5mb/XRsQRadtgskWK/9GJ/citO9fgezOz+iKCQw45BOD9iNgaXL/MrHSUyoxmqzJ/JVVJelbS5ZKelnSfpJ557R0taZakeZJGpnN6SbpK0lRJT+W1NVbSnZIeBB5o4H3vxnKHzcweeughevToAfBWbpvrl5mVilIZaDaX+TuUbDD66zT7CbANcGlEDAAWAYfmnbdOStT4FnBV2vZD4MGIGJnaOl9Sr7RvKHBYRHy2gfdN9aGOpBMlTZc0fWFtbet/A2ZWlObNm8ewYQ0F/BRn/aqtXdj6X4KZlaySuHXehFzm7+7ASlbP/H0pImal1zPIcn1zJgJExCOS1pO0AbAvcKCk09MxawObp9f3R8Tbeefnv2+sD2/kdzQiJgATAKorK724qZkVZf2qrKx2/TKzOqUy0HwaOKyB7fmZv8slzWdV5m/9XN/8W0/1C2WQFdxDI+L5/B2SduTjmcH575vqg5mVuQEDBnDzzTc3tMv1y8yKXqncOm/vzN8xqY1dgXcj4l3gXmBc7raRpCEtbGv9NvbBzMrAnnvuyYcffgjQN7fN9cvMSkVJzGh2QObvMklPAT2A49O2nwIXAnMkdQNeAg5oQVvXA39pTR/WGjSIKke4mZUFSdx2221suumm60n6F0VevwYNWovp06ta2FUzK3XOOi9Azgo2Kz9y1rmZFamm6ldJzGiWGkdQdg5HN5q1P0dQmnWdzoiUbK1SeUbTzMzMzAqMB5qJWhFhKWm0pLs6u49mZo1xDTOzQuSBJqtFWE6KiK0jYhjwfVatWdfe16voiHbNrDy5hplZofJAM9NYhOWjks5PUW5zJY3JO2c9SXdLel7SZembnEj6Q0rIeFrSObmDJc2X9EtJM4HDO+uDmVlZcA0zs4LkLwNlGouw/BJQAwwmW+NumqRH0r6RwA7Ay8A96dibgR9GxNvpL/4HJFVHxJx0zsIU5fYxaQ3QEwH6V3iywMxapUtrWH79qqjo324fysyKn2c0m7YrMDEiaiPiTeBhYETaNzUiXoyIWrLIt13T9i+nv/ifAgaQFfKcGxu7UERMiIjhETG8jweaZtY+OqWG5devioo+HfJBzKw4eaCZeRoY1spzPhbzJmlL4HRgr4ioBu5m9bi2+lFvZmbtwTXMzAqSB5qZxiIsFwFjJFVI6gfsDkxNh4yUtGV6rmkM8CiwHlkhflfSRsDnO/EzmFn5cg0zs4LkZzRpMsLyNKA3MJvsr/8zI+INSdsD04BLgE8DDwG3RcTKFP32HPAK8Fhb+uMISjNrjUKqYY6gNLN8jqAsQI5wMys/jqA0s2LlCMoi4whKs8Y5OrSwOYLSSlEhRjsWCz+jaWZmZmYdossGmq2JS0vHn58WED5fUj9JT0p6StJujRx/jaTD6m1b3IZ+tvocMyttb7zxBkcccQRbb701w4YNY//99+cf//hHo8efccYZDBgwgDPOOIO33nqLHXfckSFDhjB58uQGj3f9MrNS0SW3zvPi0q6NiCPStsFkcWmNVesTgQ0jolbSEcDciPh6B/dRHdR294hY0RFtm1nHiggOOeQQjj32WG644QYAZs+ezZtvvsm22zb8t/KECRN4++23qaio4IYbbmDQoEFcccUVHdZH1y8zKxRdNaPZqrg0SXeSfXNyRvpG5a+AgyTNktQr/fWfO+fbzV1cUm9JD0iamc45KG2vSnFsfwLmAZul7b9Ns6kPpCVCkFQj6QlJcyTdJukTafskScPT676S5qfXYyXdKelB4IH2+TWaWWd76KGH6NGjByeddFLdtsGDB7PrrrtyxhlnMHDgQAYNGsSNN2Zrmx944IEsXryYYcOG8ctf/pIzzzyTO+64g5qaGpYsWcLYsWMZOHAgwA6uX2ZWarrqy0CtikuLiAMlLY6IGgBJbwLDI+IUScOATSNiYNq3QV5750v6UQPXWQYcEhHvSeoLPJEGswDbAMdGxBOpvV7A9Ij4tqQfA2cDpwB/AsZFxMOSfpK2n9bM5x4KVEfE2/V3yBGUZkVh3rx5DBv28bXRb731VmbNmsXs2bNZsGABI0aMYPfdd+fOO++kd+/ezJo1C4CNNtqI6dOnc8kllzBjxgxeffVV5s2bh6RngKvzmizK+uUISjPLV2hfBmoqLq0xLwJbSbpY0ueA9/L2nRERNbl/edsF/FzSHODvwKZkt+0BXs4V6WQlq2LX/gzsKml9YIOIeDhtv5ZsIeTm3N9QkQZHUJoVu0cffZQjjzySiooKNtpoIz772c8ybdq0Js/ZaqutePHFFxk3bhxki6UXff1yBKWZ5euqgWZb4tIaFBHvkM2ATgJOAlry4NNRQD9gWCrgb7IqZq25iLXmFh5dwarf69r19jm+zazIDRgwgBkzGroh03qf+MQnmD17NqNHj4asJrl+mVlJ6aqBZlvi0hqUbh11i4hbgB+R3d5pzvrAfyNiuaQ9gC2aOLYbkPv251eARyPiXeCdvG+8H002+wpZGkduEL3at0bNrPjtueeefPjhh0yYMKFu25w5c9hggw248cYbqa2t5a233uKRRx5h5MiRTba1YMECVq5cyaGHHgrwKq5fZlZiuuQZzdbGpTXT3KbA1cryegG+34IuXA/8RdJcYDpZ3FpjlpBlAv8I+C9ZJjDAscBlktYhu31/XNp+AfB/aRB9dwv68jGOoDQrXJK47bbbOO200/jlL3/J2muvTVVVFRdeeCGLFy9m8ODBSOJXv/oVG2+8cZNtvfrqqxx33HGsXLkSYCvg0BZ0oaDrlyMozSyfIygLkCPczMqPHEFpZkWqqfrlCMoC5AhKs6Y5hrJwOYLSipVjJjtGoX3r3MzMzMxKRFEPNCXVpkXbn5Y0W9J38p7VbGkbG0j6Vkf10cysMRUVFdTU1DBgwADIFmx3DTOzklLUA01gaVpjbgCwD/B5soWHW0RSd2ADoFOLdLqumZW5nj17MmvWLJ5++mnI4nddw8yspBT7QLNORPyXLJniFGWqJE1OMW0zJe0MIGl02n4n8AxwHrB1mhk9P+1/WNIdkl6UdJ6koyRNTXFvW6d2qiQ9mCLcHpC0eTPbr5F0maQnySI0zczyrcA1zMxKTEn9VRoRL0qqAD5JtpTHPhGxTNI2wEQg942oocDAiHhJUlV6XQNZESdbAP4zwNtkS39cEREjJf0PMI5sGaaLgWsj4lpJxwMXAQc3sR3gU8DOEVFbv+9yBKVZ2SvWGiZHUJpZI0pmRrMBPYDL01pzNwE75O2bGhFNfW11WkS8HhEfAv8C7kvb5wJV6fUo4P+l19eRxWc2tR3gpoYGmeAISjP7mKKpYY6gNLPGlNSMpqStgFqymYCzyaLZBpMNqJflHdpclNqHea9X5r1fyZr9zhzhZmaNcg0zs1JTMjOayiIrLwMuiWwV+vWB1yNiJVnEWmPThO8D67bhko8DR6TXRwGTm9luZtaU7riGmVmJKfYZzZ6SZpHdYlpBdpvnN2nf74FbJB0D3EMjf4lHxEJJj0maB/yNlseujSOLvjwDeItVEW6NbW8xR1CalYelS5dSU1PD8uXLAbYF/kyR1zBHUJpZPkdQFiBHuJmVHzmC0syKVFP1q2RunZuZmZlZYSn2W+clyVnnVq6cYV78nHVu1rByzVIvuxlNtUNsZQuv81dJG7R3u2ZWvly/zKzYlOOM5tK8hY0/SbZe3Hq0MPZNUveIWNHccRGx/5p00sysAa5fZlZUym5GM19bYyslnSHp1LTvt5IeTK/3lHR9ej1fUt/U5rOSLk+zEPdJ6tlFH9nMSoTrl5kVg7IeaEIW+Ua2Pl1+5NtQYAxZ9FrOUOB/ImJbsnXldkvbhwO9JfVI2x5p4DLbAJdGxABgEXBo/QMknShpuqTpC2sbDA8yM1tNIdav2tqF7fLZzKw0lOOt86b0AC6RVEOWzrFt3r78yLcZwDBJ65ElbswkK9i7Aac20O5LETEr79yq+gdExARgAkB1ZaXXnDKz1iqI+lVZWe36ZWZ1yn6g2ZbIt4hYLuklYCxZisYcYA/g08CzDVwmPw6uFvCtJzNbY65fZlboyvrW+RrEVkJ2++l0sltNk4GTgKfCK+CbWSdw/TKzYlCOM5prHFuZTAZ+CEyJiCWSltFOmcCOoDSzRhR8/XIEpZnlcwRlAXKEm1n5cQSlmRUrR1CamZmZWacrx1vnBc8RlFbIHBNpTXEEpZWbco2WbKmim9GUtJGk/yfpRUkzJE2RdEgTx4+WdFcj++ZL6ptebyzpBkn/Su3+VdK2DZ3Xxn6fJmmd9mrPzIrTm2++yVe+8hW22morhg0bxqhRo7jtttsaPd41zMyKWVENNCUJuB14JCK2iohhwBHAp9qh3duASRGxdWr3+8BGa9jlfKcBLtJmZSwiOPjgg9l999158cUXmTFjBjfccAP/+c9/1qhd1zAzK1RFNdAE9gQ+iojLchsi4uWIuFjS2pKuljRX0lOS9qh/sqQ+KULtaUlXAEq79gCW12t3dkRMTtFu50ual9oek9rqLemBFPU2V9JBaXsvSXdLmp3OGZPi3voDD0l6qON+PWZWyB588EHWWmstTjrppLptW2yxBePGjYNsvOgaZmYlpdie0RxAlmLRkJOBiIhBkrYH7mvgttHZwKMR8RNJXwC+lrYPJEu8aMiXgBqyRZD7AtMkPQK8BRwSEe+lW1dPKMsS/hzwWkR8AUDS+hHxrqT/BfaIiAUNXUTSiWS5xfSvaGr5OzMrVk8//TRDhw5tbPcnKdIall+/Kir6N/+LMLOyUWwzmquRdGn6q3sasCvwZ4CIeA54mdUj2AB2zzvmbuCdFlxmV2BiRNRGxJvAw8AIspmEn0uaA/wd2JTsNtVcYB9Jv5S0W0S825LPEhETImJ4RAzv44GmWVk4+eSTGTx4MCNGjADoTZHWsPz6VVHRpwVdMrNyUWwDzaeBuumAiDgZ2Avo1w7tDmvlOUel6w6LiBqy6Le1I+IfqY9zgZ9J+vEa9s3MSsSAAQOYOXPVTZlLL72UBx54gLfeemtNm3YNM7OCVGwDzQeBtSV9M29b7uH0yWSFk3S7aXPg+XrnPwJ8JR3zeeATee1Wpts/pP3VknZL7Y6RVKEs8m13YCpZ3Nt/U27wHsAW6bz+wAcR8WfgfFYNjN8H1l3Dz29mRWzPPfdk2bJl/OEPf6jb9sEHH+ReLsY1zMxKTFE9oxkRIelg4LeSziR7xmgJ8F3gDuAPkuaSRbONjYgPsy9j1jkHmCjpaeBx4N957R4CXCjpu8AyYD7ZtywfBUYBs4EAzoyINyRdD/wlXW868Fy6xiDgfEkrgeVAblA8AbhH0msR8bGH/PM5gtKsNEni9ttv59vf/ja/+tWv6NevH7169eKXv/wlRxxxxH+BbsVewxxBaWb5HEFZgBzhZlZ+5AhKMytSTdWvYrt1bmZmZmZFoqhunZcLR1BasXAcpdXnCEqztinVKMuynNGUtLje+7GSLmnmnGskHbYG19xA0rfaer6ZWY5rmJkVi7IcaHY2Sd2BDQAXaTMrOq5hZtZWHmjWI6lK0oOS5qR4ts3zdu8tabqkf0g6IO/4ySnGbaakndP20Wn7ncAzwHnA1pJmSTq/8z+ZmZUD1zAzKyTl+oxmT0mz8t5vCNyZXl8MXBsR10o6HrgIODjtqwJGAluTZf5+GvgvsE9ELJO0DTARyH3zaigwMCJeklSVXtc01CFHUJpZKxRUDXMEpZk1plwHmkvzi6WksawqrKPIsoEBrgN+lXfe/0XESuAFSS8C2wMvAZdIqgFqWT0ybmpEtOjbEhExgWydOqorK73mlJk1paBqWH79qqysdv0yszrlOtBsq/oFNIBvk0W3DSZ7FGFZ3v4lndQvM7OWcA0zs07lZzQ/7nHgiPT6KLL4tpzDJXWTtDWwFVk83PrA62mW4Gigsfvejm8zs87gGmZmBcMDzY8bBxwnaQ5Z0f2fvH3/JssI/htwUkQsA34PHCtpNtltqAZnACJiIfCYpHl+kN7MOpBrmJkVDEdQFiBHuJmVH0dQmlmxcgSlmZmZmXU6fxmoAJVzBKUjDc2KmyMoi1upxiBa1ynqGc38GDZJ+6dFiLdo4viDJe2Q936SpDW6VSXp8TU538zKU+/evete//WvfwUY6PplZqWmqAeaOZL2IluU+PMR8XIThx4M7NDE/tZcsztAROzcHu2ZWXl64IEHOPXUUwFecP0ys1JT9ANNSbsDlwMHRMS/0rYTJE2TNFvSLZLWSbFqBwLnpwi1rVMTh0uammZDd0vnV0g6P7UxR9I30vb6kWx1s6pp3yRJN0t6TtL1kpT2DZP0sKQZku6VtEln/o7MrDA98sgjnHDCCdx1110AH4Lrl5mVlmIfaFYCtwMHR8RzedtvjYgRETEYeBb4WkQ8ThbRdkZE1OQGpUD3iBgJnAacnbZ9DXg3IkYAI4ATJOUemhwK/E9E5Kdn5AxJ7exAtkbdLpJ6kEXCHRYRw4CrgHPrnyjpRGUZxNMX1ta25XdhZkXkww8/5OCDD+b2229n++23z99V1PWrtnZh234hZlaSiv3LQMvJFif+GquvFTdQ0s+ADYDewL1NtHFr+jmDLAcYYF+gWtJh6f36wDbARzQdyTY1Iv4DkHKIq4BFwEDg/jRBUAG8Xv9ER1CalZcePXqw8847c+WVV/K73/0uf1dR1y9HUJpZvmIfaK4Evgw8IOkHEfHztP0aslnO2SkDeHQTbXyYftay6vchYFxErFbgJY2m6Ui2D/Ne59oT8HREjGrms5hZGenWrRv/93//x1577cXPf/7z/F3X4PplZiWi2G+dExEfAF8AjpL0tbR5XeD1dNvnqLzDWxqhdi/wzXQ+kraV1KuNXXwe6CdpVGqrh6QBbWzLzErIOuusw9133831118P0Ddtdv0ys5JR9ANNgIh4G/gc8CNJBwJnAU8CjwH5z27eAJwh6am8h+kbcgXZw/IzJc0D/kgbZ38j4iPgMOCXyiLeZgH+pqeZAbDhhhtyzz33AGzi+mVmpcYRlAXIEW5m5UeOoDSzItVU/SqJGU0zMzMzKzzF/mWgklTOEZTWMRztaZ3FEZRmDSvXeM+CndFUXrxkoZJ0mqR1urofZlZY8uMlC5Xrl5l1hoIdaBaJ0wAXajMrRqfh+mVmHayoBpqSvijpyfSty79L2ihtHy/p9Lzj5kmqSv+elXS5pKcl3SepZzrm06mN2ZJm5r7FKemMvOi2c9K2XpLuTsfOkzRG0qlAf+AhSQ+l446UNDcd88u8/iyWdG46/4lcv82sfPzlL39hxx13ZMiQIey99968+eabAIwfP54LLrig7jjXLzMrJUU10AQeBXaKiCFkS32c2YJztgEujYgBZCkXh6bt16ftg8mW63hd0r7p+JFADTBMWZb654DXImJwRAwE7omIi4DXgD0iYg9J/YFfAnumc0dIOjhdqxfwRLrWI8AJ9TspR1CalbRdd92VJ554gqeeeoojjjiCX/3qVy05rejqlyMozSxfsX0Z6FPAjZI2AdYCWvINh5ciYlZ6PQOokrQusGlE3AYQEcsAUqHeF3gqHd+brHBPBn6d/sq/KyImN3CdEcCkiHgrtXU9sDtZFvtHwF15fdin/smOoDQrbf/5z38YM2YMr7/+Oh999BFbtuwLf0VXvxxBaWb5im1G82LgkogYBHwDWDttX8Hqn2XtvNcNxao1RsAvIqIm/ft0RFwZEf8AhgJzgZ9J+nEr+708Vi1Y2lwfzKwEjRs3jlNOOYW5c+fyxz/+kWXLlgHQvXt3Vq5cmX+o65eZlYxiG2iuD7yaXh+bt30+WSFF0lCgyamCiHgf+E/u1pCkyvTty3uB4yX1Tts3lfTJdFvpg4j4M3B+7lqsHgk3FfispL6SKoAjgYfX4LOaWQl599132XTTTQG49tpr67ZXVVUxc+ZMwPXLzEpPIf9luo6k/+S9/w0wHrhJ0jvAg6wqyLcAx0h6miy67R8taP9o4I+SfgIsBw6PiPskfQaYIglgMfBV4NPA+ZJWpmO/mdqYANwj6bX0nNP3gIfIZhbujog72vjZzayIffDBB3zqU5+qe/+///u/jB8/nsMPP5xPfOIT7LnnnryU1jY99NBD+dOf/gQwADgF1y8zKyGOoCxAjnAzKz9yBKWZFamm6lex3To3MzMzsyJRyLfOy5YjKK21HDFphcIRlNZZyjXSsdgUzYympJD057z33SW9JemuZs4bnTtG9RZ2NzPrLJL46le/Wvd+xYoV9OvXjwMOOKC581zDzKxoFc1AE1gCDMwlY5Ct5fZqE8d3mvQtTTOzRvXq1Yt58+axdOlSAO6///66b6F3NdcwM+soxTTQBPgr8IX0+khgYm5Hilm7StJUZRGVBzXSxmBJUyS9IOmEdK4knZ+i1+ZKGpO2j86fMZV0iaSx6fV8Sb+UNBM4PL0/R1kc3FxJ27eyX2ZW4vbff3/uvvtuACZOnMiRRx6Zv7uba5iZlZpiG2jeABwhaW2gmmwpo5wfAg9GxEhgD7LlPHo10EY1WczaKODHaY25L5HFrg0G9k7nbtKC/iyMiKERcUN6vyAihgJ/AHK3t1rULzmC0qzkHXHEEdxwww0sW7aMOXPmsOOOO+bv3oQirWFyBKWZNaKoBpoRMQeoIpvN/Gu93fsC35M0C5hElq6xeQPN3BERSyNiAdmacSOBXYGJEVEbEW+SLVQ8ogVdurHe+1vTzxmpny3uV0RMiIjhETG8T4XvYpmVourqaubPn8/EiRPZf//96+9ejyKtYfn1q6KiTwsua2blohi/dX4ncAEwGsivaAIOjYjn8w+WtFG98+svHNrUQqJNRVtC9txovlxcXH5MW4P9MrPydOCBB3L66aczadIkFi782Oyfa5iZlZSimtFMrgLOiYi59bbfC4xTisSQNKSR8w+StLakPmSD1WnAZGCMpApJ/YDdySLZXgZ2UBbxtgGwVxv629J+mVkZOP744zn77LMZNGhQ/V3v4RpmZiWm6GY0I+I/wEUN7PopcCEwR1I34CWgoXVD5pDdbuoL/DQiXpN0G9nzTrPJZgfOjIg3ACT9HzAvtfdUG7rc0n6ZWRn41Kc+xamnntrQrteAHriGmVkJcQRlAXKEm1n5kSMozaxINVW/ivHWuZmZmZkVgaK7dV4OHEFp7cGxlNYVHEFp1rRyi870jKaZmZmZdYhmB5oq0YxxSRdKejU93J7bdqCk73XQ9TaQ9K2OaNvMGqc2ZoxPmjSp7pjx48dzwQUXdGg/W8s1zMyKQUtmNEsuYzwV5kOAV4DP5rZHxJ0RcV4Dx6/RIwbp/A0AF2mzTlbIGeO1bUwBcw0zs2LR0lvnpZYxPhp4mixm7ci864yVdEl6fY2kyyQ9Cfwqzcpe18rPMFnSncAzwHnA1pJmSTq/hb93M2sHTWWML1myhOOPP56RI0cyZMgQ7rjjjgbbmD17NqNGjWKbbbbh8ssvByAiOOOMMxg4cCCDBg3ixhuzoJ382VCAU045hWuuuQaAqqoqvvvd7zJ06FBuuukmqqqqOPvsswE+4xpmZqWmpX/l3kCWqXsXWc7uVcBuaV8uB/d4ZQsCT5X09wbaqAZ2AnoBT0m6m2zdtxqyfN6+wDRJj7SgPwtTHi+SziPl86bbOqcDX2+sXxGxhFWD5TuAn0vqERHLG7jOp4CdI6JW0vg2fIahwMCIeElSVXpd09AHknQicCJAf0dQmrWrI444gp/85CcccMABzJkzh+OPP57JkycDcO6557Lnnnty1VVXsWjRIkaOHMnee+/9sTbmzJnDE088wZIlSxgyZAhf+MIXmDJlCrNmzWL27NksWLCAESNGsPvuuzfbnz59+jBz5kwAvve979G3b1+AZ8lqa9HVsPz6VVHRv9nPb2blo0UzmqWUMS5pLWB/4PaIeA94EtivkevcFBH597Za+xmmRkSLvvrrrHOzjtNUxvh9993HeeedR01NDaNHj2bZsmX8+9///lgbBx10ED179qRv377sscceTJ06lUcffZQjjzySiooKNtpoIz772c8ybdq0ZvszZsyY1d5/6Utfyr0syhrmrHMza0xrntspiYxxSV8ke9ZorrJEtXWApUBDX26qf53WfIaGzjezLtJYxnhEcMstt7Dddtutdvybb7652vtULxp9n6979+6sXLmy7v2yZctW29+rV6/V3ldWVuZeuoaZWUlpzfJGpZIxfiTw9YioiogqYEtgH0nrtKDN1nyG+t4H1m3D5zCzdtBYxvh+++3HxRdfTC4l7amnGk5pvOOOO1i2bBkLFy5k0qRJjBgxgt12240bb7yR2tpa3nrrLR555BFGjhzJFltswTPPPMOHH37IokWLeOCBB9rSZdcwMyt6LZ7RLIWMcUlfBj4HnJT3uZZIehT4YgvabPFnyD3Qn3edhZIekzQP+FtEnNHYRdYaNIgqR7iZtavGMsbPOussTjvtNKqrq1m5ciVbbrkld9318cnB6upq9thjDxYsWMBZZ51F//79OeSQQ5gyZQqDBw9GEr/61a/YeOONAfjyl7/MwIED2XLLLRkypLG/v5tUlDVs0KC1mD69qpUf1cxKlbPOWyg9SL84Ijp8MT1nBZuVH3Vw1nln1TDXL7Py01T9cgRlAXIEZfMcr2hWmBxBaVa4uiL+0gPNFoqI8V3dBzOztnINM7OuUDZZ52omSlN50W1qJDJTUlV6PsnMrNO4fplZsSqnGc26KM2IWEq9KM2IuJNsCad2Jami3jp2Zmat5fplZkWpbGY0k6aiNOui2/JJGiZptqTZwMmNHS/pLkmj0+vFkn6dzhkl6ceSpimLeJuQW67EzKwVXL/MrOiU20DzBuAISWuTRbE92YJzrgbGRcTgVlynF/BkRAyOiEeBSyJiREQMBHrSwPJPkk6UNF3S9IW1nkAws48pivpVW7uw/m4zK2NlNdBsJkrzY9Ji8RtERC7397oWXqoWuCXv/R6SnpQ0F9gTGNBA3xxBaWaNKpb65QhKM8tXTs9o5jQWpdlaTcVkLss915RmH34PDI+IV9JadvUjNc3MWsL1y8yKSlnNaCaNRWl+TEQsAhZJ2jVtOipv93ygRlI3SZsBIxtpJleUF0jqDRzWpl6bmbl+mVmRKbsZzSaiNBtzHHCVpADuy9v+GFk85jPAs8DMRq63SNLlZHGab5BlCzfJEZRm1pBiqF+OoDSzfI6gLECOcDMrPx0dQdlZXL/Myo8jKIuMIyg7jqMrzTqWIyjN2q4rIiI7Wjk+o2lmZmZmnaDsB5rNRbuZmRUy1zAzK2RlP9AkL9otvV8t2q0lJPkRBDPrKq5hZlawPNDMNBXttqGk2yXNkfSEpOq0fbyk6yQ9BlwnqZ+kW1JU2zRJu6Tjekm6StJUSU9JOqizP5yZlTzXMDMrSB5oZpqKdjsHeCoiqoEfAH/K27cDsHdEHAn8DvhtRIwADgWuSMf8EHgwIkYCewDnS+pVvwOOoDSzNdClNcwRlGbWGN8uIYt2k1RFw9Fuu5IVXSLiQUl9JK2X9t0ZEUvT672BHSTlzlsvLXC8L3CgpNPT9rWBzcnWrsvvwwRgAkB1ZaXXnDKzFuvqGpZfvyorq12/zKyOB5qrtCXabUne627AThGxLP8AZVX70Ih4vj06aWbWCNcwMys4vnW+SmPRbpNJ0W2SRgMLIuK9Bs6/DxiXeyOpJr28FxiXijWShrRrr83MMq5hZlZwPKOZNBHtNp4swm0O8AFwbCNNnApcmo7rDjwCnAT8FLgQmCOpG1ns2wFN9cURlGbWWoVSwxxBaWb5HEFZgBzhZlZ+HEFpZsXKEZRFxhGUZi3nWNHC4ghKKyWlGAnZ2fyMppmZmZl1iA4ZaHZmJJqksZL617vWzyW9IGlW+vfDdrzWJY3se7w9rmFmXUsSX/3qV+ver1ixgn79+nHAAU0+Wt0m11xzDa+99lr+tV2/zKykdNSM5hpHorXCWKB/3vufpfeDIqIG2A3o0UHXrhMRO7f0WGU8m2xWgHr16sW8efNYujRbXvL+++9n00037ZBr1R9o4vplZiWmI4tFWyPRTs87bp6kqvTvWUmXS3pa0n2Seko6DBgOXJ/+8u8FnACMy60FFxHvR8T4vDZvlzQjtXNi3vbFks5P2/8uaaSkSZJelHRg3ufaLG1/QdLZ+efnvT4jRbjNkXRO2lYl6XlJfwLmAZut8W/YzDrE/vvvz9133w3AxIkTOfLII+v2vf322xx88MFUV1ez0047MWfOHADGjx/PBRdcUHfcwIEDmT9/PvPnz+czn/kMJ5xwAgMGDGDfffdl6dKl3HzzzUyfPp2jjjqKmpoayOqx65eZlZSOHGi2NRKtMdsAl0bEAGAR2QLCNwPTgaPSX/9bA/+OiPebaOf4iBhGNkA9VVJuYeNeZDFrA4D3yWYW9gEOAX6Sd/5IspSNauBwSat9y0rSvqmvI4EaYJik3fM+w+8jYkBEvFzvPEdQmhWII444ghtuuIFly5YxZ84cdtxxx7p9Z599NkOGDGHOnDn8/Oc/55hjjmm2vRdeeIGTTz6Zp59+mg022IBbbrmFww47jOHDh3P99dcza9YsgEpKoH45gtLM8nXYQDMi5gBVNB6Jdl067kEgPxKtMS9FxKz0ekZqu0mSjkszna9Iyv0Ffqqk2cATZH+Vb5O2fwTck17PBR6OiOXpdf617o+IhSm27db0WfLtm/49BcwEts+7xssR8URDfY2ICRExPCKG96moaO6jmVkHqq6uZv78+UycOJH9999/tX2PPvooRx99NAB77rknCxcu5L33Glr/fJUtt9wyN2vJsGHDmD9/frN9KNb6VVHR0lAiMysHHb28UWsj0Vaw+uB37bzXH+a9rgV68nH/BDaXtG665XQ1cLWkeUCFslSMvYFREfGBpEl511geqxYVXZm7XkSslJT/e6q/8Gj99wJ+ERF/XG1jlkO8BDMrCgceeCCnn346kyZNYuHC5mfpunfvzsqVK+veL1u2KsmxsrKy7nVFRUXd85/1fAhs4fplZqWkox/obm0k2nxgaNo+FGjJYpLvA+sCRMQHwJXAJemWPZIqgLXSsesD76QivT2wUxs+0z7pGdOewMHAY/X23wscL6l3uv6mkj7ZhuuYWRc6/vjjOfvssxk0aNBq23fbbTeuv/56ACZNmkTfvn1Zb731qKqqYubMmQDMnDmTl1qwvue6667L++/X3SlfieuXmZWYDp3RbEMk2i3AMZKeJnum8x8tuMw1wGWSlgKjgB+SRabNk/Q+sBS4FngNeB04SdKzwPNkt59aa2rq56eAP0fEahEYEXGfpM8AU5RFAy8Gvko2C9sijqA063qf+tSnOPXUUz+2ffz48Rx//PFUV1ezzjrrcO211wJw6KGH8qc//YkBAwaw4447su222zZ7jbFjx3LSSSfRs2dPyGYTi75+OYLSzPI5grIAOcLNrPzIEZRmVqSaql+OoCxAjqC0UubIyNLmCEorNY6hXDNedNfMzMzMOkRBDTRVwNGVakFEm6QrJO3QzDEnSWp+4T0zKyrqxOhK1y8zKxaFduu8LroyrfPW0dGV88gesodsgeONyaLflklaF/hO7uCWRLRFxNdbcMxlbeqtmRW0/OjKnj17dmh0Ja5fZlYkCmpGMynU6MrF6edoZRFuN0t6TtL1Sl/PTNuH546XdK6k2amvGzXUVzMrHe0RXen6ZWalpBAHmoUaXZlvCHAasAOwFbBLA8f0Ap6IiMHAI2T/I2iUHEFpVvTaO7qSIqxfjqA0s3wFN9As4OjKfFMj4j8RsRKY1UibHwG5Z0ubva4jKM2KX3tHV1KE9csRlGaWr+AGmkkuunJicwcmrYmubOi51LroSoCIuDrNFLwLNDTqa0mb+ZFwjR1jZiUmF12Zf9u8KfWjK3H9MrMSUqgDzUKLrjQza5E1ia50/TKzUlOQf6UWYHRlp3IEpVnxamt0JTAAOIUir1+OoDSzfI6gLECOcDMrP3IEpZkVqabqV6HeOjczMzOzIleQt87LnbPOrZA4m9xaw1nnVk6cg968opvRVBZT+eu896dLGt+FXcr1Y7SknfPee2FjM1uNJL7znbrAHi644ALGjx/fdR1KXL/MrKMU3UCTbGmOL0nq29UdqWc00GzMm5mVr8rKSm699VYWLFjQ1V2pbzSuX2bWAYpxoLkCmAB8u/4OSf0k3SJpWvq3S9o+V9IGyiyUdEza/idJ+0iqkHRBin6bI2lc2v/j1M48SRPyotpOlfRMOvYGSVXAScC300LJu9Xr19aS7pE0Q9JkSdt36G/IzApS9+7dOfHEE/ntb3/b4G7XLzMrNcU40AS4FDhK0vr1tv8O+G1EjAAOBa5I2x8ji1kbALwI5ArpKOBx4ESy5IuaFG95fdp/SUSMiIiBQE/ggLT9e8CQdOxJETEfuCxduyYiJtfr1wSyHOJhwOnA7+t/IEdQmpWHk08+meuvv5533323/q7NKIH65QhKM8tXlF8Gioj3JP0JOJVsvbicvYEd0h/uAOtJ6k220PvuwMvAH4ATJW0KvBMRSyTtDVwWEStS+2+n8/eQdCawDrAh8DTwF2AOcL2k24Hbm+pruv7OwE15/aps4DNNICvoVFdWes0psxK13nrrccwxx3DRRRfRs2fP1XaRLbpe974Y61dlZbXrl5nVKdYZTYALga8BvfK2dQN2Sn+V10TEphGxGHiEbBZgN2AS8BZwGFkBb5CyhI3fA4dFxCDgclZFw32BbFZ1KDBNUlMD9m7Aorw+1UTEZ1r9ac2sZJx22mlceeWVLFmypP4u1y8zKylFO9BMf7X/H9lgM+c+YFzujaSadOwrQF9gm4h4EXiU7BbQI+nQ+4Fv5AqupA1ZVZQXpL/qD0v7ugGbRcRDwHeB9YHe5EXC1evne8BLkg5P50vS4DX9/GZWvDbccEO+/OUvc+WVV+Zvfg/XLzMrMUV56zzPr8ki23JOBS5VFlHZnawQn5T2PQlUpNeTgV+QFWzInoXaFpgjaTlweURcIulyYB7wBjAtHVsB/Dk9HyrgoohYJOkvwM2SDiLvfxbJUcAfJP0I6AHcAMxu7EM5gtKs9H3nO9/hkksuyd/0CjC82OuXIyjNLJ8jKAuQI9zMyo8cQWlmRaqp+lW0t87NzMzMrLAV+63zklSKEZSOMTQrD46gLB2OV7T24BnNVlAT8ZeSrpF0WAPn9Jd0c3o9WtJdndZhM7M8rmFm1tk80GydVsdfRsRrEfGx4m1m1gVcw8ysU3mg2TqNxl8mu0t6XNKLuZkBSVWS5nVaD83MGucaZmadygPN1mss/hJgE2BXsqi381rTqCMozayTtHsNcwSlmTXGA81WSgsY5+Iv67s9IlZGxDPARq1sd0JEDI+I4X0qKpo/wcysDTqihuXXr4qKPu3VVTMrAR5ots2FfDz+ErLnn3KEmVlhuhDXMDPrBB5otkEj8ZdmZkXBNczMOosHmm33a7L8YTOzYuQaZmYdzhGUBcgRbmblxxGUZlasHEFpZmZmZp3OEZQFqBQjKK08OXq0/DiC0kqR4zjbrqBnNJuKS2uHtsdKuqSRffPzkzPaM3atsZg3MystkvjOd75T9/6CCy5g/Pjx7dW265eZFYWCHmjShri0fJI8Y2tmXaKyspJbb72VBQsWtOl81y8zKwWFPtBsNC4txaI9KGmOpAckbZ62XyPpMklPAr+SNFLSFElPpWi17fKa6S/pHkkvSPpVSzokqZekqyRNTW0elNefyZJmpn87p+2SdImk5yX9HfjkGv5OzKwIdO/enRNPPJHf/va3H9s3f/589txzT6qrq9lrr73497//Dbh+mVnpKYa/mC8F5jRQSC8Gro2IayUdD1wEHJz2fQrYOSJqJa0H7BYRKyTtDfwcODQdVwMMIZs5fV7SxRHxStr3kKRcFmRv4Ln0+ofAgxFxvKQNgKmpAP8X2CcilknaBpgIDAcOAbYDdiBL2ngGuKr+h5R0InAiQH8nA5mVhJNPPpnq6mrOPPPM1baPGzeOY489lmOPPZarrrqKU09dLaSnqOtXRUX/1v+izKxkFfxAMyLek5SLS1uat2sU8KX0+jogfyB6U0Tkiuz6wLWpeAbQI++4ByLiXQBJzwBbALlCvUdELEj7RgOnp+37AgdKyr1fG9gceA24RFINUAtsm/bvDkxM/XlN0oONfM4JZLO3VFdWes0psxKw3nrrccwxx3DRRRfRs2fPuu1Tpkzh1ltvBeDoo4+uPxAt6vpVWVnt+mVmdQp+oJlcCMwErm7h8UvyXv8UeCgiDpFUBUzK25cft1ZLy34fAg6NiOdX25h9SelNYDDZIwnLWthXMythp512GkOHDuW4445r6SmuX2ZWMgr9GU2g0bi0x4Ej0uujgMmNnL4+8Gp6PbYdunMvME6SACQNybvO6xGxEjgayN3/fgQYI6lC0ibAHu3QBzMrEhtuuCFf/vKXufLKK+u27bzzztxwww0AXH/99ey2226Nne76ZWZFrSgGmkn9uLRxwHGS5pAVxv9p5LxfAb+Q9BTtM4P7U7LbV3MkPZ3eA/weOFbSbGB7Vs1K3Aa8QPZs05+AKe3QBzMrIt/5zndW+/b5xRdfzNVXX011dTXXXXcdv/vd7xo71fXLzIqaIygLkCPczMqPHEFpZkWqqfpVTDOaZmZmZlZEiuXLQGXFEZRmH+c4y+LgCEorVo6Z7BhdMqOpdoiWlFQp6e+SZkka08RxkyQNT68XN7C/StK8Zq71sfPaoiXXMrPCp3aIl/zwww/Ze++9qamp4cYbb2zqWq5hZla0uurWeXtESw4BiIiaiGi8SpuZtbM1jZdcsWIFTz31FACzZs1izJhG/1Y2MytqXTXQXNNoyQnAn4ERaUZza0l7pUi1uSlirbKxi0vqm2LdvlBv+4AUzTYrXX+bevt7pz7NTNfJj297VtLlkp6WdJ+knmnfMEmz07c5T16zX5uZFYK2xEuOHTuWk046iR133JETTzyRr371q0ybNo2amhr+9a9/AazrGmZmpaYrvwx0KXCUpPXrbc9FS1YD15NFS+bkotmOB74OTI6IGrJ15q4BxkTEILJnT7/Z0EUlbQTcDfw4Iu6ut/sk4HepzeHAf+rtXwYcEhFDydaT+3VuPTpgG+DSiBgALGJVTNzVwLiIGNz4ryKLcJM0XdL0hbW1TR1qZgXg5JNP5vrrr+fdd99dbXsuXnLOnDkcddRRq8VL/uc//+Hxxx/nqquu4oorrmC33XZj1qxZbLrppgBbUqQ1LL9+1dYubOwwMytDXTbQjIj3yNZlO7XerlHA/0uvrwN2zduXH82WbzvgpYj4R3p/LVl0Wn09gAeAMyPi/gb2TwF+IOm7wBYRsbTefgE/T2t3/h3YlCz/l3T9Wen1DKBKWZbwBhHxSN7naVBETIiI4RExvI+zzs0KXn68ZL4pU6bwla98BcjiJR999NG6fYcffjgVDfz3/fzzzwN8WKw1LL9+VVT0aegQMytTXb280YVkaT+9Wnj8kuYPadIKsgK6X0M7I+L/AQeSZar/VdKe9Q45CugHDEszBm+SZQVD2+LgzKyInXbaaVx55ZUsWdKy0tSrV0tLXaNcw8ysqHTpQHMNoyXzPU/21/en0/ujgYcbuiRwPLB9+ot/NZK2Al6MiIuAO4DqeoesD/w3IpZL2gPYoqlORcQiYJGk3KzsUS34LGZWJNYwXrLOdtttB7CWa5iZlZquntGEtkdL1omIZcBxwE2S5gIrgcsaObYWOBLYU9K36u3+MjBP0ixgINmt/XzXA8PTNY4Bnmuub6lfl6Y21cyxZlZk1iBess7aa68NMB/XMDMrMY6gLECOcDMrP3IEpZkVqabqVyHMaJqZmZlZCfLD3gXIEZSW49hFKzaOoDRrWrlFXRbtjKbaIcYynfd4G69fJekrbTnXzMpbQxGWQP82tOP6ZWYFrWgHmrRPjCURsXMbr18FuFCbWautaYSl65eZFYtiHmg2FWN5jaTD8t4vTj9HS5os6U7gmXr7JOl8SfNSNNuYprYD5wG7pai3b0saK+kOSZMkvSDp7Lzr3y5pRop2O7GDfh9mViSairB0/TKzUlLsz2heCsyR9KtWnDMUGBgR9R9++xJQAwwmW25pmqRHgJ0b2f494PSIOABA0lhgJNmSIh+k4+6OiOnA8RHxtrLs4GmSbomI1XLaUgE/EaC/k4HMSt7JJ59MdXU1Z555ZmtOK/j6VVHR6icAzKyEFfOMZlMxlk2Z2kCRhizqcmJE1EbEm2SLJY9oYntD7o+IhSn27VZWxWeeKmk28ASwGVmmcP3P4ghKszLSWIRlMwq+fjmC0szyFfVAM7mQj8dYriB9NkndgLXy9q1pjGVT6i9KGpJGA3sDoyJiMPAUqyLfzKyMNRJh6fplZiWj6AeajcRYzgeGpdcHAj1a0NRkYIykCkn9gN2BqU1sfx9Yt14b+0jaMN1iOhh4jCzy7Z2I+EDS9sBOrf+UZlaKGoqwxPXLzEpI0Q80k/oxlpcDn023e0bRslmA24A5wGzgQeDMiHijie1zgFpJsyXlvpA0Fbgl7bslPd90D9Bd0rNkD+A/sUaf1MxKSv0IS1y/zKyEOIKynaSH6YdHxClr2pYj3MzKj7owgtL1y8zWRFP1q1RmNM3MzMyswHhGswBVV1bGnf29REgpcISktVRXzmi2p8rK6ujf/86u7oZZlyi3eMmcopnRlFSbFhCeJ+kmSet0QR+ukfRS6sdsSXvV23+apGWS1m/g3IOVRWNun7etStLS1F7u31r1zzWz4ldRUUFNTQ0DBw7k8MMP54MPPuj0PriGmVkhKaiBJrA0ImoiYiDwEXDSmjSmFNPWBmdERA1wGnBZvX1HAtPIFkiu70jg0fQz37/S58r9+6iN/TKzAtazZ09mzZrFvHnzWGuttbjssvrlo3Vcw8ys2BXaQDPfZODTknpJukrSVElPSToI6v7KnixpZvq3c9q+WkxbWtbjgjRLOkfSuHTcMEkPp2i1eyVt0kAfpgCb5t5I2hroDfyIeoVYUm+yBY6/BhzR/r8OMysmu+22G//85z9ZsmQJxx9/PCNHjmTIkCHccccdAMyfP5/ddtuNoUOHMnToUEhrAbuGmVkpKcgIyvRX/OfJltb4IfBgRBwvaQNgqqS/A/8F9omIZZK2ASYCuecD6mLaJH0TqAJqImJFWieuB3AxcFBEvKUs//dc4Ph6XfkccHve+yOAG8gGwdtJ2iilbQAcBNwTEf+QtFDSsIiYkfZtLWlWev1YRJzcwGd2BKVZiVixYgV/+9vf+NznPse5557LnnvuyVVXXcWiRYsYOXIke++9N5/85Ce5//77WXvttXnhhRfYdtttN89roqhqmBxBaWaNKLSBZs+8YjYZuBJ4HDhQ0ulp+9rA5sBrwCWSaoBaYNu8dvJj2vYGLouIFZAt8C5pIFmm7/2SACqA1/POP1/Sz4FPka1jl3MkcEhErJR0C3A4cEnevt+l1zek97ki/a90G6tRETEBmADZl4GaOtbMCtPSpUupqakBshnNr33ta+y8887ceeedXHDBBQAsW7aMf//73/Tv359TTjmFWbNmUZH9cZmfuFNUNSy/flVWVrt+mVmdQhtoLq1fzJRV0UMj4vl628cDbwKDyR4BWJa3u7kFjgU8HRGjGtl/RkTcnG5RXQUMkzSILOM3V9jXAl4iG+xuCOwJDJIUZEU/JJ3RTD/MrITkntHMFxHccsstbLfddqttHz9+PBtttBGzZ89m5cqV9OjRI/9RJtcwMysJhfyMZs69wLg04ETSkLR9feD1iFgJHE1WGBtyP/CN3EP1qaA+D/STNCpt6yFpQAPnXgJ0k7Qf2V/34yOiKv3rD/SXtAVwGHBdRGyR9m1GVsB3W/OPb2bFbL/99uPiiy8mt5TcU089BcC7777LJptsQrdu3bjuuuuaasI1zMyKVjEMNH9KlvU7R9LT6T3A74FjlcW0bU/jMwBXAP9O588GvpK+MXkY8Mu0bRawc/0TI/s/w8+AM8mebbqt3iG3pe1HNrDvFj7+zU0zKzNnnXUWy5cvp7q6mgEDBnDWWWcB8K1vfYtrr72WwYMH89xzzwGsbKQJ1zAzK1pesL0AOcLNrPyoRBZsd/0yKz9N1a9imNE0MzMzsyJUaF8GMuCjuXOZv+WWXd0NKyCOsrRiMXfuR2y55fyu7oZZUSnl6ErPaJqZmZlZhyi7gaY6KE9d0hWSdkivf5C3vUrSvPa4hpmVN9cvMys2ZTfQpJ3z1AEkVUTE1yPimbTpB02eYGbWNq5fZlZUynGgma+5PPWxkm6VdI+kFyT9KneipMWSfp2WFhklaZKk4ZLOIyUcSbo+HV4h6XJJT0u6T1LPzv+oZlZiXL/MrOCV7UBTq/LU57IqT30ksAdZfFuvdGgNMAYYBIyRtFna3gt4MiIGR8SjuXYj4nusmnU4Km3eBrg0IgYAi4BDG+jPiZKmS5q+sLa2nT+tmZWSQq5ftbUL2/nTmlkxK8dvnbcmTx3ggYh4F0DSM8AWwCtk+eq3tPCaL0VE7pozgKr6Bzjr3MxaoODrl7POzSxfOQ40W5OnviPwYd6mWlb9zpZFREunHuu34VtPZtYWrl9mVlTK9tZ5PY3lqbfVckk91rxbZmbNcv0ys4JVjjOaDfkpcCFZlnA34CXggDVob0JqaybZ81OtstagQVQ5ws3MWqag6tegQWsxfXrVGlzezEqJs84LkLOCzcqPs87NrFg1Vb88o1mAHEFpreF4SiskjqA061yFHl/pZzTNzMzMrEN4oNmANYl5S5FtX8l7P1bSJR3TUzOz1bl+mVkh8UCzYW2KeUuLKFcBX2nmUDOzjuL6ZWYFw89oNm8yUJ2SNi4GBgI9gPERcYekscCXgN5ABVAJfCYtqnwt8A7QX9I9wNbAbRFxZqd/CjMrR65fZtalPNBsQl7M2z2sink7XtIGwFRJf0+HDgWqI+JtSaOB0yPigNTGWLIYuCFkCx8/L+niiHil3rVOBE4E6F9R0bEfzMxKXlfVr4qK/h37wcysqHig2bDWxrzdHxFvN9FeYzFwdRxBaWbtpEvrlyMozSyfB5oNa23M25Jm2mssBs7MrL25fplZwfCXgVqupTFv7wPrdlqvzMya5/plZl3Cf5m2XEtj3uYAtZJmA9eQPUzfKo6gNLN21mn1yxGUZpbPEZQFyBFuZuXHEZRmVqwcQVlkHEFZnhwlaaXAEZRmbVPoUZJt5Wc0zczMzKxDFNRAszOj0yTVSApJn6u3/fG29b5FfRwryYvMmZWgiooKampqGDhwIIcffjgffPBBi8+dP38+wIa5965fZlYqCmqgSedGpx0JPJp+1omInRtpf41IqgDGAi7UZiWoZ8+ezJo1i3nz5rHWWmtx2WWXtei8FStWfGyg2QKuX2ZWFAr5Gc0Oi05LS3wcDuwDTJa0dkQsS/sWR0TvlJDx09TO9pL2JUvYmEGWpPE0cExEfCBpL+ACst/nNOCbEfGhpPnAjek6vwGGA9dLWgqMioilHfKbM7MutdtuuzFnzhyWLFnCuHHjmDdvHsuXL2f8+PEcdNBBXHPNNdx6660sXryY2tpaPvzwQ4Derl9mVmoKbUYTWC06bS6rotNGAnsA56fBJ2QF87CI+CzwPWBymhH9bdpfA4wBBgFjJG2Wtu8MvBQR/wImAV9opCtDgf+JiG3T++2A30fEZ4D3gG9JWptsGZAxETGIrFh/M6+NhRExNCL+DEwHjkp9XK1ISzpR0nRJ0xfW1rb8l2VmBWXFihX87W9/Y9CgQZx77rnsueeeTJ06lYceeogzzjiDJUuy9dFnzpzJzTffzMMPP8x5550HsLgU6ldt7cI2/NbMrFQV2kAzF502Hfg3WXTavsD30vZJtCE6Lf21n4tOg+x20w3p9Q3Uu/2UZ2pE5H8V+JWIeCy9/jOwK1nxfiki/pG2XwvsnnfOjU30r05ETIiI4RExvI+zzs2KztKlS6mpqWH48OFsvvnmfO1rX+O+++7jvPPOo6amhtGjR7Ns2TL+/e9/A7DPPvuw4YZN3i0vyvpVUdGnJaeYWZkotFvnHR6dlp41OhQ4SNIPAQF9JK0bEe/XO79++/UXHW3JIqTN9dHMSkDuGc18EcEtt9zCdtttt9r2J598kl69etEM1y8zK3qFNqPZkPaOTtsLmBMRm0VEVURsAdwCHNKCczeXNCq9/grZw/jPA1WSPp22Hw08vIZ9NLMSsN9++3HxxReTC8Z46qmnGjxu3XXXhexZ8+a4fplZUSm0Gc2GtHd02pHAbfW23UL2XNKfmunL88DJkq4iu5X1h4hYJuk44Kb0bOk0oLGvm14DXNbcw/SOoDQrDWeddRannXYa1dXVrFy5ki233JK77rrrY8dVV1cDRCnUL0dQmlk+R1C2kKQq4K609FKHcoSbWfnpyAhK1y8z60iOoCwyjqA0K37lGinqCEorNKUa7VgsPNBsoYiYT7aWp5lZUXH9MrOuUgxfBmqTNYyzrJG0f977JuPgWtmv8ZJOb4+2zKw0uX6ZWako2YEmaxZnWQPs38yhZmYdxfXLzEpCudw6z8VZbghcBWwFfACcGBFzJI0ni3nbimyh+F3IFo/fFfhFfkOSvgj8CFgLWEiWlPFmamPz1MbmwIURcVE654fAscB/gVfIYuDMzFrC9cvMilbJDzTz4izvAc4BnoqIgyXtSbYcSE06dAdg14hYmnLUh0fEKamNsXlNPgrsFBEh6evAmcB30r7tyWIy1wWel/QHoBo4Il2nOzCTBgq1pBOBEwH6OxnIzCjO+lVR0b89PrqZlYhSHmjm4iwhmxG4EniSLFWDiHhQUh9J66Vj7mxsXbh6PgXcKGkTslmB/K+W3h0RHwIfSvovsBGwG3BbRHwAIOnOhhqNiAnABIDqykqvOWVW3oq2flVWVrt+mVmdUh5oNhRn2dTxLY1auxj4TUTcKWk0MD5v38ci41rYpplZPtcvMysJpfxloIZMBo4CSEV2QUS818BxTUWtrQ+8ml4f24JrPgIcLKmnpHWBL7amw2ZmieuXmRWdcvuLdTxwlaQ5ZA/TN1ZoHwK+l25d/aLevvFkcW3vAA8CTa6sHhEzJd0IzCZ7mH5ac510BKWZNWA8RVC/HEFpZvkcQVmAHOFmVn46MoKyM7l+mZWfpupXud06NzMzM7NOUm63zouCs85LV7nmX1v5cNa5WeuUehZ7Uc5o1otn+4ukDdL2/pJububc+ZL6dkpHC/D6Zta1KioqqKmpYeDAgXzxi19k0aJFuV09XL/MrNQU5UCT1ePZ3gZOBoiI1yLisK7tmplZ43r27MmsWbOYN28eG264IZdeemlu13LXLzMrNcU60Mw3BdgUQFKVpHnpdYWkC9Ks5xxJ4/LOGSdppqS5krZPx4+XdK2kyZJelvQlSb9Kx9wjqUc6bi9JT6XtV0mqTNvnSzqngXb7SLpP0tOSrgCaXAzPzMrHqFGjePXV3GpDrOX6ZWalpqgHmpIqgL2AhtIqTgSqgJqIqAauz9u3ICKGAn8ATs/bvjWwJ3Ag8GfgoYgYBCwFviBpbeAaYEza3h34ZjPtng08GhEDgNvIcoQb+iwnSpouafrC2toW/gbMrFjV1tbywAMPcOCBBza0u2jrV23twpb+CsysDBTrQDMXz/YGWUza/Q0cszfwx4hYARARb+ftuzX9nEFWzHP+FhHLgblABVm+MOl9FbAd8FJE/CNtvxbYvZl2dycr+kTE3cA7DX2giJgQEcMjYngfZ52blaylS5dSU1PDxhtvzJtvvsk+++zT0GFFW78qKvo08snNrBwV60AzF8+2BdmtnJNbeX4uaq1+zNqHABGxkux5qdwioytp2Tf0G2vXzAxY9Yzmyy+/TETkP6PZUq5fZlY0inWgCUBEfACcCnxHUv3CeD/wjdx2SRu2wyWfB6okfTq9Pxp4uJlzHgG+kvrweeAT7dAPMyty66yzDhdddBG//vWvWbFiRf3drl9mVhKK/q/WiHgqRbIdSZYFnHMFsC0wR9Jy4HLgkjW81jJJx5FFuHUni2O7rJnTzgEmSnoaeBz4d3PXcQSlWXkYMmQI1dXVTJw4sf6uoq1fjqA0s3yOoCxAjnAzKz+OoDSzYuUISjMzMzPrdEV/67wUOYLS2sLxllYIHEFp1nKlHj8JntFsMUk/TIsWz0nxlzs2cew1kpzwYWYFwzXMzLqCZzRbQNIo4ABgaER8mLJ+1+ribpmZtYhrmJl1Fc9otswmZKkZuXXqFkTEa5J+LGlaiombIOlj8WyShkl6WNIMSfdK2qTTe29m5c41zMy6hAeaLXMfsJmkf0j6vaTPpu2XRMSIiBgI9CSbMaiT8oUvBg6LiGHAVcC5DV3AEZRm1oE6tIY5gtLMGuNb5y0QEYslDQN2A/YAbpT0PeB9SWcC6wAbAk8Df8k7dTtgIHB/miioAF5v5BoTgAkA1ZWVXnPKzNpNR9ew/PpVWVnt+mVmdTzQbKGIqAUmAZMkzQW+AVQDwyPiFUnjgbXrnSbg6YgY1Zl9NTOrzzXMzLqCb523gKTtJG2Tt6mGLM4NYIGk3kBD39B8HuiXHsRHUg9JAzq0s2Zm9biGmVlX8Yxmy/QGLpa0AbAC+CdwIrAImAe8QRbntpqI+CgtEXKRpPXJft8Xkt2eapQjKM2snXVaDXMEpZnlcwRlAXKEm1n5cQSlmRUrR1CamZmZWafzrfMC5AjKwuaoR7PGOYLSrGMUa1xll8xoFloUmqTukn4u6YXUn1mSftiR1zSz4nXuuecyYMAAqqurqamp4cknn2z02LFjx3LzzTd3aH9cw8ysUHX6jGaBRqH9DNgYGBQRyyStC3yni/tkZgVoypQp3HXXXcycOZPKykoWLFjARx991NXdcg0zs4LUFTOaaxKFNl/SL9Jf69MlDU2RaP+SdFI6RpLOT+3MlTQmbR8taZKkmyU9J+n6dOw6wAnAuIhYlvr0fkSMT+dVSZqX14fT03pzpPZ+m/ryrKQRkm5Nswo/S8f0knS3pNmpT2M68HdrZh3s9ddfp2/fvlRWVgLQt29f+vfvz09+8hNGjBjBwIEDOfHEE2noi5ZVVVV8//vfp6amhuHDhzNz5kz2228/tt56a4B+4BpmZqWlKwaabYpCy/PviKgBJgPXkK39thNwTtr/JbI14gYDewPna1U27xDgNGAHYCtgF+DTqc332/h5PkrftLoMuAM4mSxJY6ykPsDngNciYnD6bPc01IgcQWlWFPbdd19eeeUVtt12W771rW/x8MMPA3DKKacwbdo05s2bx9KlS7nrrrsaPH/zzTdn1qxZ7LbbbnW31Z944gmA/umQoqthcgSlmTWi0weaEbEYGEa2httbZFFoY4E9JD2pLLFiT6CxRYHvTD/nAk+mv9zfAj5UtkbcrsDEiKiNiDeBh4ER6ZypEfGfiFgJzAKq6jcu6bg0Y/qKpM1a8JHy+/N0RLyeZmtfBDZL2/eR9EtJu0XEu438XiZExPCIGN6noqIFlzWzrtC7d29mzJjBhAkT6NevH2PGjOGaa67hoYceYscdd2TQoEE8+OCDPP10w0tNHnjggQAMGjSIHXfckXXXXZd+/foBrCzWGpZfvyoq+rTgkmZWLrrkW+dtjELL+TD9XJn3Ove+uc+Tf3xtOv6fwOaS1k2D1quBq9OtpgqyxY3zB+T1+9VkfyLiH5KGAvsDP5P0QET8pJl+mlkBq6ioYPTo0YwePZpBgwbxxz/+kTlz5jB9+nQ222wzxo8fz7Jlyxo8N3fLvVu3bnWv87iGmVlJ6fQZTbU9Cq2lJgNjJFVI6gfsDkxt7OCI+AC4ErhE0tqpjxWs+oLSm8AnJfWRVEnjt/QbJKk/8EFE/Bk4Hxja2g9kZoXj+eef54UXXqh7P2vWLLbbbjsge15z8eLFa/otc9cwMysZXTGj2aYotFa4DRgFzAYCODMi3pC0fRPn/BD4KTBP0vvAUuBasueSlkv6CVmhfxV4rpX9GUT2jNVKYDnwzVaeb2YFZPHixYwbN45FixbRvXt3Pv3pTzNhwgQ22GADBg4cyMYbb8yIESOab6hxrmFmVjIcQVmAHOFmVn7kCEozK1JN1S9HUJqZmZlZh3AEZQFyBKWVI0d7lgZHUJq1r2KNnswpqxlNtSL6st55YyVd0tH9MzNrjOuXmRWjspnRVGFGX5qZNcv1y8yKVTnNaH4s+hLYXtLtuQMk7SPptvT6uJReNJUsfSN3zDWSLpL0uKQXJR2Wt+8MZTGacySdk7b9RNJpececK+l/Ovizmllpcf0ys6JUTgPNhqIvHyIr1v3SMccBV6W4t3PICvSuZHFv+TZJ2w8AzgOQtC+wDTCSbG3QYZJ2B64CjknHdAOOAP5cv3NyBKWZNa5o6pcjKM0sX9kMNBuKvgSOBa4DvprW9RwF/A3YEZgUEW9FxEfp2Hy3R8TKiHgG2Cht2zf9ewqYCWwPbBMR84GFkobk9kfExyqxIyjNrDHFVL8cQWlm+crmGU1oMPryWLL4y78Ay4CbImKFpOaayo9pU97PX0TEHxs4/gpgLLAx2QyBmVmruH6ZWTEqmxnNRqIvX46I14DXgB8BV6d9TwKfTZFtPYDDW3CJe4HjU4QmkjaV9Mm07zbgc8CIdJyZWYu5fplZsSqnGc3Goi8Brgf6RcSzABHxuqTxwBSyaMxZzTUeEfdJ+gwwJc0oLAa+Cvw3Ij6S9BCwKM1KmJm1huuXmRUlR1ACaY25pyLiyg5qvxvZc0+HR8QLzR3vCDez8tPWCErXLzPrao6gbIKkGUA1DXyTsp3a34Fs9uGBlhRpM7OWcv0ys0LnGc0CVF1ZGXf279/V3TBrlOMi219bZzQLTWVldfTvf2dXd8OsYBR7hGRLdPmMZiFGp0k6TdIySevnbRsu6aKOuF5q/wcd1baZdYxzzz2XAQMGUF1dTU1NDU8++WSLzrvmmms45ZRTOqRPrl9mViw6/MtABRyddiQwDfgS6duaETEd+NjDRZK6R8SKtl5I2dP1An4A/Lyt7ZhZ55oyZQp33XUXM2fOpLKykgULFvDRRx91dbfA9cvMikRnzGgWXHSapK3JvsX5I7KCnTtmtKS70uvxkq6T9BhwXZpdvUPSJEkvSDo777z/lTQv/TstbauS9LykPwHzgCuBnmlG9/p2+c2aWYd6/fXX6du3L5WVlQD07duX5557joMPPrjumPvvv59DDjkEgKuvvpptt92WkSNH8thjj9UdM3bsWE499VR23nlnttpqK26++ea6feeffz4jRowA2MH1y8xKTWcMNAsxOu0I4AZgMrCdpI1o2A7A3hGRK+YjgUPJHr4/PN2qGpb6vyOwE3CCshQNUr9+HxEDIuI4YGlE1ETEUfUvJEdQmhWcfffdl1deeYVtt92Wb33rWzz88MPssccePPfcc7z11ltANrg8/vjjef311zn77LN57LHHePTRR3nmmWdWa+v111/n0Ucf5a677uJ73/seAPfddx8vvPACU6dOBXiGEqhfjqA0s3wdPtAs0Oi0I4EbImIlcAuNL2h8Z0QszXt/f0QsTNtuJRv07grcFhFL0me9FdgtHf9yRDzRwt+TIyjNCkzv3r2ZMWMGEyZMoF+/fowZM4Zrr72Wo48+mj//+c8sWrSIKVOm8PnPf54nn3yS0aNH069fP9Zaay3GjBmzWlsHH3ww3bp1Y4cdduDNN98EsoHmfffdx5AhQyAbGBZ9/XIEpZnl65QF2wspOk3SILK/1O9P11sLeAlo6EtHS+p/lGbeN3e+mRWZiooKRo8ezejRoxk0aBDXXnstf/zjH/niF7/I2muvzeGHH0737s2X0tztd4Dcah8Rwfe//32+8Y1vIOmZet/adP0ys6LX4TOaKrzotCOB8RFRlf71B/pL2qIF19pH0oaSegIHA4+R3b46WNI6knoBh6RtDVmePpeZFYHnn3+eF15YtXzkrFmz2GKLLejfvz/9+/fnZz/7GccddxwAO+64Iw8//DALFy5k+fLl3HTTTc22v99++3HVVVexePFiwPXLzEpPZ8xoFlp02hHA/vWauS1tb27dkqlkt6o+Bfw5fcsTSdekfQBXRMRTkqoaOH8CMEfSzIaeczKzwrJ48WLGjRvHokWL6N69O5/+9KeZMGECAEcddRRvvfUWn/nMZwDYZJNNGD9+PKNGjWKDDTagpqam2fb33Xdfnn32WUaNGgXZrfObcf0ysxLSpQu2q8Ci05ppaywwPCI6ZmG8PI5wMyt8p5xyCkOGDOFrX/tau7Snegseu36ZWbGoX7/ydVkEpRydZmZFatiwYcyZM4evfvWrHdK+65eZlQpHUBYgR1AWD0cxWntpakagmDiC0qzjFGqcZUHOaLZF+pLQrPTvDUmv5r3v9LQhSf0l3Zxe5y+W3GHRmWZWvBYuXEhNTQ01NTVsvPHGbLrppnXvWbWSRqdxDTOzjtYpyxu1l7SOXA1kyRfA4oi4oAv78xpwWLMHmpkBffr0YdasWQCMHz+e3r17c/rppwMgqdNvL7mGmVlHK6oZzYZI2kvSU5LmSrpKUqWkEZJuTfsPkrRU0lqS1pb0Yto+SdIvJU1NqUW7pe0D0rZZyiItt5F0nqST8645XtLpymLa5jXTv36SblEWkTlN0i5NHW9m5eWBBx6oW7DdNczMSk2xDzTXBq4BxkTEILIZ2m+SpQTVpGN2I8vqHUGWPJS/BEj3iBgJnAbksn9PAn4XETXAcOA/ZAlFX84778t8PLWoMb8DfhsRI8ji365o6CA5gtKs7CxbtoyxY8dy4403QhZBWZQ1TI6gNLNGFNWt8wZUAC9FxD/S+2uBkyPiQkn/SutrjgR+A+yejs9fjPjW9HMGUJVeTwF+KOlTwK3pG59PSfqkpP5AP+CdiHilkbXm6tubbKYi9349Sb1T3FudiJhAtk4d1ZWV/oaWWRmora1lyy23ZNttt81tKsoall+/KiurXb/MrE6xDzSb8gjweWA58Heymc8K4Iy8Y3KRlrWk30VE/D9JTwJfAP4q6RsR8SBwE9mzTBvT8pkAyGaNd4qIZW3/KGZWhlzDzKzoFfut81qgStKn0/ujgYfT68lkt5OmRMRbQB9gO7JbUI2StBXwYkRcBNxBttYnZIX5CLJC3Xy23Cr3AePy2q9pxblmVsIqKiqYP38+//znP3ObXMPMrKQU+0BzGXAccJOkucBK4LK070lgI7JZAYA5wNxofuHQLwPzJM0CBgJ/AoiIp4F1gVcj4vVW9PFUYHh6KP8ZsuenzMxYe+21ufrqqzn88MMhi6B0DTOzkuIF2wuQI9zMyk+pLNju+mVWfkpmwXYzMzMzKx6l/GWgovXR3LnM33LLru6GdQFHWlqxmzv3I7bccn5Xd8Os6BVq3GRreUbTzMzMzDpEl8xoSuoDPJDebkz27fG30vuREfFRV/TLzKw5CxcuZK+99gLgjTfeoKKign79+gEwdepU1lprra7snplZQemSgWYhZJZL6h4RKzrzmmZW/JrKK+8srl9mViwK5ta5pGskHZb3fnH62U3S7yU9J+l+SX/NHSdp/7R9hqSLJN2VtvdKmcFTUw76QWn7WEl3SnoQeEBSb0kPSJqpLCs9d1yVpGclXS7paUn3SeqZ9tVIeiIt9XGbpE+k7Y3lDq8j6f8kPZOOf1JS0X+z1MxWGTt2LDfffHPd+969ewOwcuVKvvWtb7H99tuzzz77sP/++9cd99e//pXtt9+eYcOGceqppwJ8Gly/zKy0FMxAswlfIotW24FsMeNRAJLWBv4IfD4ihpHFquX8EHgwZQDvAZwvqVfaNxQ4LCI+S7YO5yERMTQd92utylnbBrg0IgYAi8gyfiFbk+67EVENzGVVvjA0nDv8LbK4tx2As4BhDX1IOevcrOTceuutzJ8/n2eeeYbrrruOKVOmAFnG+Te+8Q3+9re/MWPGDN56663804q6fjnr3MzyFcNAc1fgpohYGRFvAA+l7duTpV/kvqY7Me+cfYHvpQWLJwFrA5unffdHxNvptYCfS5pDFvG2KdkCyZBlqM9Kr2eQJRCtD2wQEbnkjmvJ8odzGsod3hW4ASAi5pEtuvwxETEhIoZHxPA+FRWN/zbMrGg8+uijHH744XTr1o2NN96YPfbYA4DnnnuOrbbaii3T6hJHHnlk/mlFXb8qKvo092sxszJSSMsbrSANfCV1A9bkiXoBh0bE86ttlHYEluRtOopsJnRYRCyXNJ+sqMOqDGHIvqzUswXX/VjusJmVvu7du7Ny5Uogu13+0Udr9H1G1y8zKxmFNKM5n1W3ZQ4EeqTXjwGHpmc1NwJGp+3PA1tJqkrvx+S1dS8wLncbSdKQRq65PvDfVKT3ALZoqoMR8S7wTu75JVbPJW7MY2SRcEjaARjUzPFmVmSqqqqYMWMGAHfeeSfLly8HYJddduGWW25h5cqVvPnmm0yaNAmA7bbbjhdffJH58+cDcOONN+Y35/plZiWjkP5qvRy4Q9Js4B5W/eV+C7AX8AzwCjATeDcilkr6FnCPpCXAtLy2fgpcCMxJs6MvAQc0cM3rgb8oy0mfDjzXgn4eC1wmaR3gRbKs9ab8HrhWWUbwc8DTwLtNnbDWoEFUOcLNrGiccMIJHHTQQQwePJjPfe5z9OqVPVJ56KGH8sADD7DDDjuw2WabMXToUNZff3169uzJ73//+7pjR4wYAdlMIhR5/Ro0aC2mT69qQVfMrBwURda5pN4RsVjZ+ptTgV0i4o287QIuBV6IiN92bW9XJ6kC6BERyyRtTfYs1XZNrRXqrGCz0rF48WJ69+7NwoULGTlyJI899hgbb7xx3faI4OSTT+YPf/jDKxGxefMtdh7XLzNrCTWRdV5IM5pNuUvSBmTPbf40fSkI4ARJx6btT5F9C73QrAM8JKkH2bNX32puQfrOjqB07KFZxznggANYtGgRH330EWeddRYbb7wxAJdffjnXXnstH330EUOGDAFY0KUdbVir65cjKEtbqcQiWucpihnNclNdWRl39u/fadfzQNOs6zU1I1BMKiuro3//O7u6G9ZBPNC0hpTCjGanSV8uuisiBuZtGw8sBgamfTc3fLaZWddx/TKzQlNI3zovOZI8kDezouT6ZWbtwQPNNpI0X1Lf9Hq4pEnp9XhJ10l6DLguxcZdknfeXZJGd0WfzczA9cvMOo8Hmh1jB2DviDiy2SMTR1CaWYFYo/rlCEozy+eB5sc19u2o1nxr6s6IWNqqizqC0szWXJfXL0dQmlk+DzQ/biHwiXrbNuTjS4/URWayKvYtZ0kjxzV0rJlZe3H9MrOC4oFmPRGxGHhd0p4AkjYEPgc8Wu/Q+ayKzDy0iSbnAzUpQnMzYGS7dtjMLHH9MrNC428VNuwY4FJJv0nvz4mIf6Xo4ZxzgCsl/RSY1ERbj5FFyD0DPEsWodkkR1Ca2Rro0vrlCEozy+cF2wuQI9zMyk+pLNju+mVWfrxge5Hp7AhK63hOX7Jy4QhKs7YrxeQlP6NpZmZmZh2i0waakn4r6bS89/dKuiLv/a8l/W8H9+EgSbfnvf++pH/mvf+iJIf0mtlqvv3tb3PhhRfWvd9vv/34+te/Xvf+O9/5Dr/5zW8aOLP9uH6ZWTHqzBnNx4CdASR1A/oCA/L27ww83sF9eBzYKe/9KOA9SZ/sxD6YWZHZZZddePzxrDT8//buPUqq8l7z+PcRFEQQ5JAxIirE68FuBCMBGo0az5Bj4kSdqPHoGDCKMcZMYtSzyMrkRM2sI5Pb5AoaNWDMRQ3RxINzEhI1inaLoCBNc4ka8RYTJQJCFOTymz/2W03R6W76Vt1Vu5/PWqyu2rXr3futKn799t673mfnzp2sW7eOhoaGxsdra2upqakp9W64fplZxenOgWYtWWGEbIC5Atgk6QBJ/YB/BKZIWixphaQfKH1NUtJ4ScslLZP0NUkr0vIBku6WtFLSvZIWSTohPTY7JVU0SLoeICJeJyvMR6T9OBj4BWkAnH4+lo4MLJK0VNLvJB2Y2nyXpN+mNm+V9EJRjNuXJK2R9Kikn0m6Ji2fnvr0tKRfSBpQyhfZzLpeTU0NdXV1ADQ0NFBVVcWgQYNYv349W7duZdWqVSxYsIDx48dTVVXFZZddRuGLlosXL2bMmDGMHTuWa6+9lqqqKgDeeustzjvvPEaPHs3ZZ58NcIzrl5nlTbcNNCPiT8B2SYeSFcQ6YBHZ4PMEoB74XkSMj4gqYF/gjPT0OcAnI2IsUJzPeAWwPiJGA19i17xwAF9M34AaA5wsaUxa/hhQI+lo4Bng8XS/L3AcsJhszrmJETEOuBP41/TcLwMPRsSxwDzgUMgGwmRz0R0HnJ76U3BP6tNxZNODXNLc6+MISrPyNXz4cPr27cuLL75IbW0tkyZNYsKECdTV1bFkyRKqq6u58sorWbx4MStWrODtt99m/vz5AFx88cXcfPPNLFu2jD5FqV+zZs3igAMOYOXKlXzlK18B2K9okxVbvxxBaWbFuvvLQLVkg8zCQLOu6P5jwKnpL/F64APAsZKGAIMioi618dOi9k4kK6RExApgedFj50l6ClhKdgR1dAv78AQwARgHrI6ILcAI4DdpP65l1yn+4u39Gliflk8GfhURWyJiE/AfRftRJWlhautCdr9coJEjKM3KW01NDbW1tY0DzUmTJjXenzx5Mg899BATJkygurqaBx98kIaGBjZs2MCmTZuYNCk7mXPBBRc0tvfoo49y/vnnAxSOcr5VtLmKrV+OoDSzYt090Cxcp1lNdur8cbIjmoVri2YB50RENXALHYw7kzQKuAY4LSLGAPcXtVXYhxqgLhXW/sAp7Lq+6btkR1ergU92dD+SucCVqa3rO9mWmfWQwnWa9fX1VFVVMXHiROrq6hqvz7ziiiuYN28e9fX1TJ8+nS1btnRoO65fZpYnPXFE8wzgjYjYERFvAEPIBpuFIrlO0kDgHICI2EB2LeeE9Pj5Re09BpwHIGk02QAWYH+yvN6N6fqk04ueswoYTvbX/dK0bBlweWoPYDDwSro9tYXtTWFXpvBjwH+T1D/t+xlFzxlEFgm3N9kRATOrQDU1NcyfP5+hQ4fSp08fhg4dyoYNG6irq2v8ItCwYcPYvHkz8+bNA2DIkCEMGjSIRYsWAXDnnXc2tjd58mTuvvtuAFauXAnZ5ULg+mVmOdLdE7bXk33b/KdNlg2MiHWSbiE70vlnsmuNCi4BbpG0E3gY2JiWzwJul7QSWA00ABsj4hlJS9Oyl9hVgImIkLQIGBwR29LiOuAydg12rwN+Lmk98CBQmD39euBnki5Kz/kzsCkiFiubVmQ58JfUp8I+fonsWtTX089Be3qRHEFpVn6qq6tZt27dbqe/q6ur2bx5M8OGDWP69OlUVVXx7ne/m/Hjxzeuc9tttzF9+nT22msvTj75ZAYPHgzAFVdcwdSpUxk9ejTHHHMMwBZyUL8cQWlmxSoiglLSwIjYnG7PAA6KiM9K6gPsHRFbJB0O/A44OiLeKdF+9AN2RMR2SZOA2ekLSo37mL6V+QhwWUTsMRe4OY5wM8uPzZs3M3DgQABmzpzJq6++yre//W127NjBtm3b6N+/P8899xxHHHHEO2TXo7t+mVlFUQ4iKD8s6Qtk+/sCMC0tHwA8lE7rCLiiVEU6ORS4W9k8oO8A04se+0E6fd8fuL2jRRocQWnW1XoyAvT+++/nxhtvZPv27Rx22GHMnTsXyKY3OvXUU9m2bVthKqQX8lC/HEFp5SSPkY6VpiKOaPY2Y/r1i/uGD+/p3TDLjUrImm/tiEAl6ddvTAwf7oAiKw8eaHaP1upXrrPO1YnYS0lzJZ3Twe1eV5jw2MysI1y/zCwPcj3QpDxiL83MOsL1y8wqXt4Hmh2OvSwmaa2kr0qql/SEUgScpJGSHlQWj/lASj1q+tzDJf1a0pNp4uNjStddM8sR1y8zq3i5Hmh2MvayqY1p0uLvAd9Ky75LduH8GOAnwHeaed4PgM9ExHvJJmGe1VzjcgSlmRWp1PrlCEozK5brgWbS7tjLFtr5WdHPwlGGSeyaE/QOskmUG6XJj2vI5rRbBtwMHNRc446gNLNmVFz9cgSlmRWrlOmNOqNp7OVLwNXAm8AcsqjLEyLiJUnX0XLEWrRwuzV7ARsKc9WZmbWT65eZVbTeckSzXbGXLfhY0c+6orYLkZgXAguLnxARbwLPSzoXQJnjOtcdM+tFXL/MrKL1hiOaHY29bOoAScuBrcC/pGWfAeZIupYsou3iZp53ITBb0v8C9gbuBJ5ubYcdQWlmScXVL0dQmlkxT9jeBpLWkp2eWtcd23OEm1nvU6oJ212/zKzU8hBB2as4gtLKWSWk7FjPcQSl2S5OJvJAs00iYmRP74OZWUe4fplZT8rFl4E6GdU2TVKXBotLGi5pXle2aWb5dNVVV/Gtb32r8b7rl5nlSS4GmnQuqm0a0KWFOiL+FBEdyhk2s95l8uTJ1NZm5cn1y8zyJi8DzQ5FtUk6hyxh4yeSlknaV9JMSStTLNvXoeWoNklzJX1HUq2kP6b2CuuvKLq9UNJT6V9N9740ZlbOampqqKsrzDjk+mVm+ZKLgWZHo9oiYh6wBLgwTUo8ADgbODbFsv3vtInWotoOIkvUOAOY2czuvQb814g4nmwOu+Zi3hxBadZLDR8+nL59+wLsQw7qlyMozaxYLgaaSVdEtW0EtgC3SfrvwFtpeWtRbb+MiJ0RsRI4sJk29wZuSdv9OTC6uZ13BKVZ71VTUwOwHzmoX46gNLNieRpoNo1qe5yswBaub5oFnBMR1WSxbX8X1RYR24H3AfPI/sL/dRu2u7Xotpp5/CrgL8BxZEcn9mlbd8yst5g8eTLAQFy/zCxn8jTQ7GhU2yZgEEB6bHBE/D+yAntcUdstRrXtwWDg1YjYCVwE+HClme0mHdEcguuXmeVMnubR7GhU21zgJklvA6cDv5LUn+yv+8KUIm2JamvJLOAXkj5OdoThb3t6giMozXqX6upqyOrx40WLK7J+OYLSzIo5grIMOcLNrPcpVQRld3P9Mut9WqtfeTp1bmZmZmZlJE+nznPDWefW3Zxfbl3FWefWnZwlXv7K4ohmGyMk/03SjHT/OknXpNtziyYavlVSs9NvtHN/Rkq6oLPtmFnv0DRG8oMf/CCXXnpp4/2rr76aG264gZkzs6kqr7vuOr7+9a8DMG3aNObNyxIfXcPMLG/KYqBJ2yIkF0REcxMKN4qIS9N8cJ01EnCRNrM2KY6R3LlzJ+vWraOhoaHx8draWqZMmcKMGTNabcc1zMzyplwGmm2JkBwj6XutNSLp95JOSLdnp6SKBknXF62zVtKNKbJtiaTj0xHU5yRdnlabCZyU1rlKUn9JcyTVS1oq6dTU1v2SxqTbSyX9W7p9g6Tpkk5J+zRP0mpJP5HU3Fx1ZlbBimMkGxoaqKqqYtCgQaxfv56tW7eyatUqli9fzpVXXtlqO65hZpY3ZXGNZkT8SVLTCMmDyQafG8mm+Xinnc1+MSLekNQHeEDSmIhYnh57MSLGSvq/ZNODTCabAHkFcBMwA7gmIs4AkHR1tptRLekYYIGko8jmoztJ0gvA9tQOwEnA5WTxbuPIBs9/IjtyOxl4tOnOSroMuAxguJOBzCpKIUbyxRdfpLa2lkmTJvHKK69QV1fH4MGDqa6uZp992j3XecXUsOL61afP8Pb208xyrFyOaMKeIyTb6zxJTwFLyYpk8XVP96Wf9cCiiNgUEa8DWyUNaaatE4EfA0TEauAFoFCk309WeO8HBkoaAIyKiDXpuU9ExMtpwuNlZKe0/o4jKM0qW01NDbW1tY0DzUmTJjXeT8k/7VUxNcwRlGbWknIaaO4pQrLNJI0CrgFOi4gxZAW0OLKtELu2k90j2HbSvqO8i8li2U4CHiH7hTAdeLKZbQHsaGf7ZlYhCtdp1tfXU1VVxcSJE6mrq6O2traQ/NNmrmFmlhflNNBsS4RkW+1PlmCxUdKBZIkZ7dEY65YsJItuI51uOhRYExHvAC8B55IdgV1I9svhkXZuz8wqXE1NDfPnz2fo0KH06dOHoUOHsmHDBurq6to90MQ1zMxyopz+Mt1ThGSbG4qIpyUtBVaTFdH2nnpfDuyQ9DTZ9U+zgNmS6smuY5oWEYW/8heSHXV4W9JCYATtyxL+O46gNKs81dXVrFu3jgsuuGC3ZZs3b2bYsGHtaquSa5gjKM2smCMoy5Aj3Mx6HzmC0swqVGv1q5xOnZuZmZlZjpTTqXNLHEFptovjMSuLIyit0jnWsmtVzBFNtT+m8iztIcotTUY8vx370Li+pI8UbetdkhalCY9PknSupFWSHmpnN80sh9oSUQkc5PplZnlTMQNN2h9TeRa7zzvXpSLivqJtnQbUR8S4iFgIXAJMj4hTS7V9M6scbYmoBN50/TKzvKmkgWabYyol1QAfAb6WItgOl3SEpN9JelrSU5IOT20NbC5eTdJ4SbVp/SckFU8VgqRpaVtjga8CZ6ZtfZlscuTbJH1NUp/0c7Gk5ZI+WeoXyszKS1siKoF9Xb/MLG8q5hrN9sRURkStpPuA+RExD0DSImBmRNwrqT/ZIPsQmolXk/QEcBfwsYhYLGl/4O0W9muZsnzgEyLiyrStU8ni35Yoi2bbGBHj04D4MUkLImK3C8/kCEqz3GpLROWjjz4aUPn1yxGUZlasko5oQgdjKtNf8wdHxL0AEbElIt5KDzcXr3Y08GpELE7rvxkR2zu4z1OAj0taBiwC/gE4sulKjqA0y7eORlRWWv1yBKWZFauYI5pJ05jKl4CrgTeBOcDQDrRZ6ng1AZ+JiN90cbtmVkGaRlQecsghfOMb32D//ffn4osv7mizrl9mVtYq8YhmW2MqGyPYImIT8LKkswAk9ZM0oJXtrCH7Buj4tP4gSR0t4L8BPiVp79TWUZL262BbZlah2hlR6fplZrlQaQPNQkzl402WbYyIdU3WvRO4Nk3ZcThwEfA/JS0nG5S+u6WNpPzfjwHfVRbh9lugfwf3+VZgJfCUpBXAzVTekWQz66RCROXEiRN3WzZ48ODmIipdv8wsFxxBWYYc4WbW+8gRlGZWoVqrX5V2RNPMzMzMKoRPgZQhR1BaT3Lko3WGIyitt3OE5e7K7oim2hY1+fkWnvt7SSek22sl/d2FT2ZmpdKWqMlvfvObLT39aNcvM8ubshto0raoyabfMDcz63FtiZps5hvmZma5VY4DzbZETQ5O38asl/TDtLxFkj4vaUX697m0bKSkVZJukdQgaYGkfdNj41Pc2rIUv7YiLe8vaU7a7tKUoFGIc7tH0q8lPSPpq0XbvkTSH1IM3C2SvtfFr5eZlYm2RE1u3LiRcePGUV1dzSc+8Qm2bt3aapuuX2ZWycpuoBkRfwKaRk0uIht8ngA8QzblxscioprsOtNPtdSepPcCFwMTgInAdEnj0sNHAt+PiGOBDcBH0/I5wCcjYizZJMgFn852MaqBfwFuT3FwAGPJphSpBj4m6RBJw4Evpe1OBo5pZT8vk7RE0pK/7tjR0mpmVsaai5qcMGECdXV1LFmyhCOPPJJLL72Uu+66i/r6erZv387s2bNbbK8S69eOHX9t46tlZr1B2Q00k9aiJl8Gno+IP6R1bwfe30pbJwL3RsTfImIzcA9wUnrs+YhYlm4/CYyUNAQYFBF1aflPm7T1Y4CIWA28AByVHnsgIjZGxBayeecOA94HPBwRb0TENuDnLe2kIyjN8qG1qMkRI0YwatQojjoqKxtTp07lkUceaa25iqtfjqA0s2LlOtBsGjX5ONkRzRrg9124na6Mbyt1FJyZVYCmUZMTJ06krq6O2tpaTjnllK7clOuXmZW9ch1othY1+Quyv9yPSOteBDzcSlsLgbMkDUjRaWenZc2KiA1k14ROSIvOb9LWhZBFsQGHksW9tWQxcHK6vrQvu05tmVlOtRY1+dGPfpS1a9fy7LPPAnDHHXdw8sknt9ac65eZVbRyHWi2FjX5Mtk1Sz+XVA/sBG5qqaGIeAqYCzxBdq3nrRGxdA/bvwS4RdIyYD9gY1o+C9grbfcuYFpEtHglf0S8Avx72vZjwNqitswsh1qLmhwxYgRz5szh3HPPpbq6mr322ovLL7+8xbZcv8ys0jmCshmSBqbroZA0AzgoIj7bmbbSEYF7gR9GxL2tPccRbma9T1dFULp+mVl3a61++Tqc5n1Y0hfIXp8XgGmdaOs6Sf8E9AcWAL/s9N6ZmbXM9cvMyoaPaJahMf36xX3Dh/f0buyRowrNuk5XHdHsaf36jYnhw+/r6d0ws05ob4xma/WrXK/RbBN1Iq7SzKynNY2sdA0zs7yp6IEmZRxXqUylv75mVkLFkZWJa5iZ5UqlF5EOx1VKOq2F5Wsl3Zji25ZIOj4dZXhOUuPXQyVdK2lxinq7Pi0bKWmNpB+lfTlE0uzUTkNhPTMz2D2yEtgX1zAzy5mKHmh2NK4yxa7Nbbq8qOkXU3zbwrTeOWQxbIViPIUs/u19ZNFt75VUSCc6EpgVEcdGxAvAF9N1C2PI5qQb01xf5AhKs16nOLKSbCqiiqxhcgSlmbWgogeaSUfiKo9uYXlB4Ur2emBRRGyKiNeBrcoi3qakf0uBp8gygI9Mz3khIorn/zxP0lNp3WOB0c11whGUZr1TIbISGEiF1jBHUJpZS/IwvVHTuMqXgKuBN8niKjuSZlGYxHgnu0ez7SR7zQTcGBE3Fz9J0kjgb0X3RwHXAOMjYr2kuWTThJiZAbtdp1k4de4aZma5kZcjmu2Nq1zTwvK2+g3wCUkDASQdLOm/NLPe/mRFe6OkA4HT29UzM8u9QmQlsMM1zMzyJg9HNAtxlT9tsmxgRLwsqRBX2Zcsu/emiNja3PK2bjAiFkj6R6BOEsBm4H8AO5qs97SkpcBqsqMUj3W0k2aWT4XISrI6UuAaZma54Anby5Aj3Mx6H+VkwnbXL7Pep7X65YFmGZK0iezUWG8wDFjX0zvRTdzXfOqqvh4WEe/qgnZ6VC+qX73hM+4+5kN39LHF+pWHU+d5tCYPRzbaQtIS9zV/3NderVfUr97wvruP+dDTfczDl4HMzMzMrAx5oGlmZmZmJeGBZnn6QU/vQDdyX/PJfe29esvr0Rv66T7mQ4/20V8GMjMzM7OS8BFNMzMzMysJDzTNzMzMrCQ80Cwzkv5Z0hpJz0qa0dP70xmSDpH0kKSVkhokfTYtHyrpt5KeST8PSMsl6Tup78slHd+zPWg/SX0kLZU0P90fJWlR6tNdkvZJy/ul+8+mx0f26I53gKQhkuZJWi1plaRJeX1vJV2VPsMrJP1MUv88v7cdlbP6tVZSvaRlkpakZRX9+Zb0Q0mvSVpRtKzdfZI0Na3/jKSpPdGXlrTQx+skvZLey2WSPlT02BdSH9dI+mDR8rL9LHfl79ZueS8jwv/K5B/QB3gOeA+wD/A0MLqn96sT/TkIOD7dHgT8ARgNfBWYkZbPAP5Puv0h4D8BAROBRT3dhw70+fNkcajz0/27gfPT7ZuAT6XbV5BFCQKcD9zV0/vegb7eDlyabu9Dls+du/cWOBh4Hti36D2dluf3toOvU97q11pgWJNlFf35Bt4PHA+s6GifgKHAH9PPA9LtA3q6b3vo43XANc2sOzp9TvsBo9Lnt0+5f5bpot+t3fVe+ohmeXkf8GxE/DEi3gHuBM7s4X3qsIh4NSKeSrc3AavIfmmfSTZIIf08K90+E/hRZB4Hhkg6qHv3uuMkjQA+DNya7gv4ADAvrdK0r4XXYB5wWlq/IkgaTFbQbwOIiHciYgM5fW/Jwi32VZYrPgB4lZy+t52Qq/rVgor+fEfEI8AbTRa3t08fBH4bEW9ExHrgt8A/l3zn26iFPrbkTODOiNgaEc8Dz5J9jsv6s9yFv1u75b30QLO8HAy8VHT/5bSs4qXTh+OARcCBEfFqeujPwIHpdqX3/1vAvwI70/1/ADZExPZ0v7g/jX1Nj29M61eKUcDrwBxllwrcKmk/cvjeRsQrwNeBF8kGmBuBJ8nve9tRFfsetyCABZKelHRZWpa7zzft71Ol9vXKdNr4h4VTyuSgj5383dot/fRA00pO0kDgF8DnIuLN4sciO35f8XNsSToDeC0inuzpfekmfclOT82OiHHA38hO1TTK0Xt7ANkRgVHAcGA/yugIjpXMiRFxPHA68GlJ7y9+MC+f72J57FMyGzgcGEv2x+I3enRvukil/G71QLO8vAIcUnR/RFpWsSTtTfYf4ScRcU9a/JfCaaX087W0vJL7Pxn4iKS1ZKdZPgB8m+wURd+0TnF/GvuaHh8M/LU7d7iTXgZejohF6f48soFnHt/bfwKej4jXI2IbcA/Z+53X97ajKvk9/jvpSDYR8RpwL9np1Dx+vtvbp4rra0T8JSJ2RMRO4Bay9xIquI9d9Lu1W/rpgWZ5WQwcqezbrPuQfZHgvh7epw5L16XdBqyKiG8WPXQfUPh221TgV0XLP56+ITcR2Fh0GqCsRcQXImJERIwke98ejIgLgYeAc9JqTftaeA3OSeuXxV+fbRERfwZeknR0WnQasJIcvrdkp8wnShqQPtOFvubyve2E3NQvSftJGlS4DUwBVpDPz3d7+/QbYIqkA9LR/ilpWdlqcr3s2WTvJWR9PF/ZTBGjgCOBJyjzz3IX/m7tnveyq79d5H+d/jbZh8i+QfYc8MWe3p9O9uVEskP3y4Fl6d+HyK5XewB4BvgdMDStL+D7qe/1wAk93YcO9vsUdn3r/D1khetZ4OdAv7S8f7r/bHr8PT293x3o51hgSXp/f0n2rcVcvrfA9cBqsl9Qd5B9SzW3720nXqdc1K/03j6d/jUU+lLpn2/gZ2SnjreRnZW4pCN9Aj6RPt/PAhf3dL/a0Mc7Uh+Wkw26Dipa/4upj2uA0yvhs0wX/m7tjvfSEZRmZmZmVhI+dW5mZmZmJeGBppmZmZmVhAeaZmZmZlYSHmiamZmZWUl4oGlmZmZmJeGBplknSNpconaHSLqi6P5wSfNae46ZWXu4fll38PRGZp0gaXNEDCxBuyPJ5uKs6uq2zczA9cu6h49omnUxSWMlPS5puaR7U+ICko6Q9DtJT0t6StLhkgZKeiDdr5d0ZmpmJnC4pGWSviZppKQVqZ3+kuak9ZdKOjUtnybpHkm/lvSMpK/2zCtgZpXK9cu6Wt89r2Jm7fQj4DMR8bCkG4AvA58DfgLMjIh7JfUn+0PvHeDsiHhT0jDgcUn3ATOAqogYC41HCAo+DUREVEs6Blgg6aj02FhgHLAVWCPpuxHxUmm7a2Y54vplXcpHNM26kKTBwJCIeDgtuh14f8pNPjgi7gWIiC0R8RZZNNi/S1pOFhl2MHDgHjZzIvDj1M5q4AWgUKgfiIiNEbGFLI/7sK7rnZnlmeuXlYKPaJr1rAuBdwHvjYhtktaSZWV31Nai2zvw/3EzKx3XL9sjH9E060IRsRFYL+mktOgi4OGI2AS8LOksAEn9JA0ABgOvpSJ9Krv+gt8EDGphMwvJCjzplNOhwJpS9MfMeg/XLysF/7Vg1jkDJL1cdP+bwFTgplSI/whcnB67CLg5Xfe0DTiX7Lqn/5BUDywBVgNExF8lPZYuoP9P4PtF25gFzE7P2Q5Mi4itkkrWSTPLJdcvKzlPb2RmZmZmJeFT52ZmZmZWEh5ompmZmVlJeKBpZmZmZiXhgaaZmZmZlYQHmmZmZmZWEh5ompmZmVlJeKBpZmZmZiXx/wGCWXMTkUqnywAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('RainTomorrow')\n",
    "sns.barplot(x = pd.DataFrame(tlog['Location']).sort_index()['Location'], y = pd.DataFrame(tlog['Location']).sort_index().index, color = \"red\")\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('Not RainTomorrow')\n",
    "sns.barplot(x = pd.DataFrame(flog['Location']).sort_index()['Location'], y = pd.DataFrame(flog['Location']).sort_index().index, color = \"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79115eec-5ee0-4de8-9803-2a0987fffe31",
   "metadata": {},
   "source": [
    "从上图可以发现不同地区降雨情况差别很大，有些地方明显更容易降雨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "92066992-721e-4379-9638-93f72c874b23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x144 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,2))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('RainTomorrow')\n",
    "sns.barplot(x = pd.DataFrame(tlog['RainToday'][:2]).sort_index()['RainToday'], y = pd.DataFrame(tlog['RainToday'][:2]).sort_index().index, color = \"red\")\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('Not RainTomorrow')\n",
    "sns.barplot(x = pd.DataFrame(flog['RainToday'][:2]).sort_index()['RainToday'], y = pd.DataFrame(flog['RainToday'][:2]).sort_index().index, color = \"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355f8020-7258-40cf-83ea-1e0e3ff7a3ab",
   "metadata": {},
   "source": [
    "上图我们可以发现，今天下雨明天不一定下雨，但今天不下雨，第二天大概率也不下雨。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548c7a26-628b-47d5-b73e-aa13449232f4",
   "metadata": {},
   "source": [
    "# Step5:对离散变量进行编码"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e19b6c-c7da-43cd-849b-c5eb25d1b25b",
   "metadata": {},
   "source": [
    "由于XGBoost无法处理字符串类型的数据，我们需要一些方法讲字符串数据转化为数据。一种最简单的方法是把所有的相同类别的特征编码成同一个值，例如女=0，男=1，狗狗=2，所以最后编码的特征值是在 [\n",
    "0,特征数量−1]之间的整数。除此之外，还有独热编码、求和编码、留一法编码等等方法可以获得更好的效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9df364ec-02f3-4fca-aadf-226ed58a0809",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 把所有的相同类别的特征编码为同一个值\n",
    "def get_mapfunction(x):\n",
    "    mapp = dict(zip(x.unique().tolist(),\n",
    "         range(len(x.unique().tolist()))))\n",
    "    def mapfunction(y):\n",
    "        if y in mapp:\n",
    "            return mapp[y]\n",
    "        else:\n",
    "            return -1\n",
    "    return mapfunction\n",
    "for i in category_features:\n",
    "    data[i] = data[i].apply(get_mapfunction(data[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b69f4cf-fff3-42dd-81a3-bce958895025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 编码后的字符串特征变成了数字\n",
    "\n",
    "data['Location'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca87cdc-5959-497a-93e8-ff06b4dd520c",
   "metadata": {},
   "source": [
    "# Step6：利用 XGBoost 进行训练与预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6575305-5a6f-43ad-a6ad-718ff253fce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 为了正确评估模型性能，将数据划分为训练集和测试集，并在训练集上训练模型，在测试集上验证模型性能。\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "## 选择其类别为0和1的样本 （不包括类别为2的样本）\n",
    "data_target_part = data['RainTomorrow']\n",
    "data_features_part = data[[x for x in data.columns if x != 'RainTomorrow']]\n",
    "\n",
    "## 测试集大小为20%， 80%/20%分\n",
    "x_train, x_test, y_train, y_test = train_test_split(data_features_part, data_target_part, test_size = 0.2, random_state = 2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9506bd77-5b0d-4713-a39c-1850193a9d73",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "## 导入XGBoost模型\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "## 定义 XGBoost模型 \n",
    "clf = XGBClassifier()\n",
    "# 在训练集上训练XGBoost模型\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e20d7400-32df-4aab-93d0-ff36185869b7",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "## 在训练集和测试集上分布利用训练好的模型进行预测\n",
    "train_predict = clf.predict(x_train)\n",
    "test_predict = clf.predict(x_test)\n",
    "from sklearn import metrics\n",
    "\n",
    "## 利用accuracy（准确度）【预测正确的样本数目占总预测样本数目的比例】评估模型效果\n",
    "print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_train,train_predict))\n",
    "print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_test,test_predict))\n",
    "\n",
    "## 查看混淆矩阵 (预测值和真实值的各类情况统计矩阵)\n",
    "confusion_matrix_result = metrics.confusion_matrix(test_predict,y_test)\n",
    "print('The confusion matrix result:\\n',confusion_matrix_result)\n",
    "\n",
    "# 利用热力图对于结果进行可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_matrix_result, annot=True, cmap='Blues')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e38dbbc-5faf-4e24-addf-b0d8e7f0562b",
   "metadata": {},
   "source": [
    "我们可以发现共有15759 + 2306个样本预测正确，2470 + 794个样本预测错误。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca3b62c0-1fa6-41c3-8fa9-114bf566c6cc",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Step7: 利用 XGBoost 进行特征选择"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6290abc8-3dae-40a5-b6d3-81f43e6c6860",
   "metadata": {},
   "source": [
    "XGBoost的特征选择属于特征选择中的嵌入式方法，在XGboost中可以用属性feature_importances_去查看特征的重要度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb588d87-ecb1-4efa-91ee-e4e591c24a42",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "? sns.barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "332ea66e-13b5-4069-9eaf-6b696e5737f2",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "sns.barplot(y=data_features_part.columns, x=clf.feature_importances_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b389d0b9-df1e-40ec-9dd6-c40ca24ff519",
   "metadata": {},
   "source": [
    "从图中我们可以发现下午3点的湿度与今天是否下雨是决定第二天是否下雨最重要的因素\n",
    "\n",
    "初次之外，我们还可以使用XGBoost中的下列重要属性来评估特征的重要性。\n",
    "\n",
    "* weight:是以特征用到的次数来评价\n",
    "* gain:当利用特征做划分的时候的评价基尼指数\n",
    "* cover:利用一个覆盖样本的指标二阶导数（具体原理不清楚有待探究）平均值来划分。\n",
    "* total_gain:总基尼指数\n",
    "* total_cover:总覆盖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ddfd197-0816-42d7-8117-8777a8363d80",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from xgboost import plot_importance\n",
    "\n",
    "def estimate(model,data):\n",
    "\n",
    "    #sns.barplot(data.columns,model.feature_importances_)\n",
    "    ax1=plot_importance(model,importance_type=\"gain\")\n",
    "    ax1.set_title('gain')\n",
    "    ax2=plot_importance(model, importance_type=\"weight\")\n",
    "    ax2.set_title('weight')\n",
    "    ax3 = plot_importance(model, importance_type=\"cover\")\n",
    "    ax3.set_title('cover')\n",
    "    plt.show()\n",
    "def classes(data,label,test):\n",
    "    model=XGBClassifier()\n",
    "    model.fit(data,label)\n",
    "    ans=model.predict(test)\n",
    "    estimate(model, data)\n",
    "    return ans\n",
    " \n",
    "ans=classes(x_train,y_train,x_test)\n",
    "pre=accuracy_score(y_test, ans)\n",
    "print('acc=',accuracy_score(y_test,ans))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4689235-bddd-4863-8221-346b81093522",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "这些图同样可以帮助我们更好的了解其他重要特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f43280-8aa9-4519-9a95-e48b1cdffa71",
   "metadata": {},
   "source": [
    "# Step8: 通过调整参数获得更好的效果"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e184c812-204a-4daf-b089-583d868e2a57",
   "metadata": {},
   "source": [
    "XGBoost中包括但不限于下列对模型影响较大的参数：\n",
    "\n",
    "* learning_rate: 有时也叫作eta，系统默认值为0.3。每一步迭代的步长，很重要。太大了运行准确率不高，太小了运行速度慢。\n",
    "* subsample：系统默认为1。这个参数控制对于每棵树，随机采样的比例。减小这个参数的值，算法会更加保守，避免过拟合, 取值范围零到一。\n",
    "* colsample_bytree：系统默认值为1。我们一般设置成0.8左右。用来控制每棵随机采样的列数的占比(每一列是一个特征)。\n",
    "* max_depth： 系统默认值为6，我们常用3-10之间的数字。这个值为树的最大深度。这个值是用来控制过拟合的。max_depth越大，模型学习的更加具体。\n",
    "\n",
    "调节模型参数的方法有贪心算法、网格调参、贝叶斯调参等。这里我们采用网格调参，它的基本思想是穷举搜索：在所有候选的参数选择中，通过循环遍历，尝试每一种可能性，表现最好的参数就是最终的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c22531ab-5572-4432-a1e8-8703ff9d3838",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "## 从sklearn库中导入网格调参函数\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "## 定义参数取值范围\n",
    "learning_rate = [0.1, 0.3, 0.6]\n",
    "subsample = [0.8, 0.9]\n",
    "colsample_bytree = [0.6, 0.8]\n",
    "max_depth = [3,5,8]\n",
    "\n",
    "parameters = { 'learning_rate': learning_rate,\n",
    "              'subsample': subsample,\n",
    "              'colsample_bytree':colsample_bytree,\n",
    "              'max_depth': max_depth}\n",
    "model = XGBClassifier(n_estimators = 50)\n",
    "\n",
    "## 进行网格搜索\n",
    "clf = GridSearchCV(model, parameters, cv=3, scoring='accuracy',verbose=1,n_jobs=-1)\n",
    "clf = clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b681cf9a-6a91-4b5a-ac0e-60910d6878f4",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "## 网格搜索后的最好参数为\n",
    "\n",
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65f7139b-98b4-46ce-9778-9160a0f70db4",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "## 在训练集和测试集上分布利用最好的模型参数进行预测\n",
    "\n",
    "## 定义带参数的 XGBoost模型 \n",
    "clf = XGBClassifier(colsample_bytree = 0.6, learning_rate = 0.3, max_depth= 8, subsample = 0.9)\n",
    "# 在训练集上训练XGBoost模型\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "train_predict = clf.predict(x_train)\n",
    "test_predict = clf.predict(x_test)\n",
    "\n",
    "## 利用accuracy（准确度）【预测正确的样本数目占总预测样本数目的比例】评估模型效果\n",
    "print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_train,train_predict))\n",
    "print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_test,test_predict))\n",
    "\n",
    "## 查看混淆矩阵 (预测值和真实值的各类情况统计矩阵)\n",
    "confusion_matrix_result = metrics.confusion_matrix(test_predict,y_test)\n",
    "print('The confusion matrix result:\\n',confusion_matrix_result)\n",
    "\n",
    "# 利用热力图对于结果进行可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_matrix_result, annot=True, cmap='Blues')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442a81e2-3efb-4513-8918-532d292947d3",
   "metadata": {},
   "source": [
    "原本有2470 + 790个错误，现在有 2112 + 939个错误，带来了明显的正确率提升."
   ]
  }
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